{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Practical-Unit4_Python.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOhLwnXQQxax22AXQnJmkFK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"8OQKGvsNaFTK"},"source":["# The Mathematical Engineering of Deep Learning\n","----\n","\n","## Practical 4 (Python version)\n","**For an R or Julia version see the [course website](https://deeplearningmath.org/)**."]},{"cell_type":"markdown","metadata":{"id":"ywk2b9U9Xykg"},"source":["# Goals\n","----\n","In this tutorial, we mainly use the MNIST dataset to explore classification deep neural networks (DNN) models.\n","At the end of this tutorial, you should be comfortable to use a software package (here **keras**) to run different models for a classification task.  You will explore different models by exploring/tuning different hyperparamaters of the DNN: \n","\n","- number of layers and nodes\n","- batch normalization\n","- regularization technique\n","- dropout \n","- weight initialization\n","- Early stopping\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"jEW0o82dHJKj"},"source":["# MNIST Data set \n","----\n","## Loading package and dataset\n","\n","First you have to install **keras** R packages using "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3yyr58ewHLWk","executionInfo":{"status":"ok","timestamp":1611287812603,"user_tz":-600,"elapsed":2679,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"1e7a4641-9dde-4b20-dcd0-33bce5f35c89"},"source":["from keras.datasets import mnist\n","(train_X,train_Y), (test_X,test_Y) = mnist.load_data()\n","print('Training data shape : ', train_X.shape, train_Y.shape)\n","print('Testing data shape : ', test_X.shape, test_Y.shape)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n","Training data shape :  (60000, 28, 28) (60000,)\n","Testing data shape :  (10000, 28, 28) (10000,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vshgGmq3YoCq","executionInfo":{"status":"ok","timestamp":1611287814600,"user_tz":-600,"elapsed":782,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"92e423c3-8e2e-4f09-e595-03f31a31b883"},"source":["# Find the unique numbers from the train labels\n","import numpy as np\n","\n","classes = np.unique(train_Y)\n","nClasses = len(classes)\n","print('Total number of classes : ', nClasses)\n","print('Classes : ', classes)\n"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Total number of classes :  10\n","Classes :  [0 1 2 3 4 5 6 7 8 9]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"7LK9m1mDYxKc","executionInfo":{"status":"ok","timestamp":1611287816320,"user_tz":-600,"elapsed":795,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"d9a552e2-6cdd-4966-9c29-e471e3890e25"},"source":["# Display the first image in training data\n","import matplotlib.pyplot as plt\n","\n","plt.figure(figsize=[5,5])\n","plt.subplot(121)\n","plt.imshow(train_X[0,:,:], cmap='gray')\n","plt.title(\"Class : {}\".format(train_Y[0]))"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Class : 5')"]},"metadata":{"tags":[]},"execution_count":3},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAKAAAACuCAYAAACxxQZdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMeElEQVR4nO3de4yU1RnH8e+vIGlQQBFFggpqCRSMbhPFakmRIBUIFgTTiJc0kXhJ2IRGS2po02oTCBGwCZE20HrDWsSmUpHUgsplNVriimgViyABZYtauS6rcn36x/ti5p13dnd2ZnbPzM7zSSaz58zZeR/cx/NezzkyM5wL5VuhA3DVzRPQBeUJ6ILyBHRBeQK6oDwBXVCegHmS9ICkP4eOo7PxBMwg6RZJ9ZIOS9oj6UVJI0LHlYskk9QUx3pY0p9Cx1SIrqEDKBeS7gXuB+4BVgNHgbHAROC1gKG15HIz2x46iGJ4DwhI6gX8FphuZs+ZWZOZHTOzF8xsZjO/81dJn0o6KKlO0rCMz8ZL2iKpUVKDpJ/H9X0krZJ0QNI+Sa9Kquq/QVX/4zNcDXwbWNGG33kRGAScC2wCns747FHgbjPrAVwKrI3r7wN2A+cAfYFZQM57oXGi3t9KDHXx/wTPSRrYhtjLhidg5GzgCzM7nu8vmNljZtZoZkeAB4DL454U4BgwVFJPM9tvZpsy6vsBA+Ie9lVr5ma8mU0ws7kthDASGAgMAf4LrJJUcYdUnoCRvUCffP+AkrpImivpI0mHgJ3xR33i9ynAeGCXpA2Sro7r5wHbgTWSduTRwzXLzOrM7KiZHQBmABcB3y30+0LxBIy8ARwBJuXZ/haik5PrgF5EPRGAAMzsTTObSLR7/jvwbFzfaGb3mdnFwI+BeyWNLtG/wU5tv5J4AgJmdhD4NbBI0iRJ3SWdJmmcpIdy/EoPooTdC3QH5pz6QFI3SbdK6mVmx4BDwMn4swmSviNJwEHgxKnP2kLSMEk1cU98BrAAaAA+aOt3heYJGDOzBcC9wK+A/wGfALVEPVi2pcAuoj/6FuBfWZ/fDuyMd8/3ALfG9YOAl4HDRL3u781sXa544muQs5oJty+wnCi5dxD1wBPihK8o8gdSXUjeA7qgPAFdUJ6ALqiiElDSWElbJW0v5pqWq14Fn4RI6gJ8CIwhur30JjDVzLaULjzX2RVz62Y4sN3MdgBIeobo4myzCSjJT7mr1xdmdk52ZTG74P5E18pO2R3XOZfLrlyV7X7zWtJdwF3tvR1XmYpJwAbggozy+XFdgpktAZaA74JdWjG74DeBQZIuktQNuBlYWZqwXLUouAc0s+OSaokeX+8CPGZm75csMlcVOvResO+Cq9pbZnZFdqXfCXFBeQK6oDwBXVCegC4oT0AXlCegC8oT0AXlCeiC8gR0QXkCuqA8AV1QnoAuKE9AF5QnoAuqqEfyJe0EGokm2Tme63Eb51pSijEho8zsixJ8j6tCFTejZjnq0qVLqq5Xr145WrautrY2Ue7evXuqzeDBgxPl6dOnp9rMnz8/UZ46dWqqzddff50oz52bnpD1wQcfbD7YEij2GNCIZvt8Kx795lybFNsDjjCzBknnAi9J+o+Z1WU28GGZriVF9YBm1hC/f040w/zwHG2WmNkVfoLicim4B5R0OvAtM2uMf/4R0VobFePCCy9M1XXr1i1Rvuaaa1JtRoxILp505plnptpMmTKlyOiat3v37kR54cKFqTY33nhjotzY2Jhq88477yTKGzZsKEF0bVPMLrgvsCKa7piuwF/M7J8licpVjWLGBe8ALi9hLK4K+Z0QF5QnoAuqqmZGqKmpSZTXrl2balPoBeT2cvJkehmRO+64I1E+fPhwq9+zZ8+eVN3+/fsT5a1bt7YxujbxmRFc+fEEdEF5ArqgquphhI8//jhR3rt3b6pNex0Dbty4MVV34MCBVN2oUaMS5aNHj6baPPXUU6ULLDDvAV1QnoAuKE9AF5QnoAuqqk5C9u3blyjPnDkz1WbChAmJ8ttvv51qk+vpk2ybN29OlMeMGZNq09TUlKobNmxYojxjxoxWt1XJvAd0QXkCuqBaTUBJj0n6XNJ7GXW9Jb0kaVv8flb7huk6q1YfRpD0Q+AwsNTMLo3rHgL2mdnceJnWs8zsF61urAKWaejZs2einOtJ4sWLFyfK06ZNS7W57bbbEuVly5aVILqKVtjDCPEgo31Z1ROBJ+OfnwQmFR2eq0qFHgP2NbNTz/d8SvR4vnNtVvRlGDOzlnatPizTtaTQHvAzSf0A4vfPm2vowzJdSwrtAVcCPwXmxu/PlyyiwA4dOtRqm4MHD7ba5s4770yUly9fnmqT62nnapPPZZhlwBvAYEm7JU0jSrwxkrYB18Vl59qs1R7QzNKz2kRGlzgWV4X8TogLqqpGxZXK6aefnii/8MILqTYjR45MlMeNG5dqs2bNmtIGVt58VJwrP56ALihPQBeUJ6ALyk9CSuCSSy5J1W3atClRzjUEc926dam6+vr6RHnRokWpNh35NyshPwlx5ccT0AXlCeiC8mPAdpI9R/Pjjz+eatOjR49Wv2fWrFmpuqVLlybKuaZeK0N+DOjKjyegC6rQUXEPSGqQtDl+jW/fMF1nlU8P+AQwNkf978ysJn79o7RhuWqRz/OAdZIGtn8oncuKFSsS5W3btqXaPPzww6m60aOTj1nOmTMn1WbAgAGJ8uzZs1NtGhoa8ooztGKOAWslvRvvon1guitIoQn4B+ASoAbYAyxorqGkuyTVS6pvro2rXgUloJl9ZmYnzOwk8EdyLFKY0dZHxblmFZSAp4Zkxm4E3muurXMtyWdumGXAtUAf4DPgN3G5hmjB6p3A3RkzJbT0XVVzJyQfuVbZvOGGGxLlXHdQ4gUiv5FrwZ1c8xEGlvNOSKGj4h4tSUiu6vmdEBeUJ6ALyp+GKXNHjhxJ1XXtmjxyOn78eKrN9ddfnyivX7++pHEVwJ+GceXHE9AF5QnogvIEdEFV1UI1IV122WWpuptuuilVd+WVVybK2SccuWzZsiVVV1dX14bowvEe0AXlCeiC8gR0QfkxYAkMHjw4VVdbW5soT548OdXmvPPOK2h7J06cSJRzDcuslPmnvQd0QXkCuqDyGZZ5gaR1krZIel/SjLjeFyx0RcunBzwO3GdmQ4HvA9MlDQXuB14xs0HAK3HZuTbJ54HUPUQDjzCzRkkfAP2JFiy8Nm72JLAeaHXFzEqT60Rh6tTkM7rZJxwAAwcOLMn2s+cLhPQwzJUrV5ZkWyG06RgwHh/8PWAjvmChK4G8L8NIOgP4G/AzMzuUOS6hpQULfbFC15K8ekBJpxEl39Nm9lxcndeChT4s07Wk1R5QUVf3KPCBmWXOJVHxCxb27Zs+ahg6dGii/Mgjj6TaDBkypCTb37hxY6pu3rx5ifLzz6f/s1bKReZ85LML/gFwO/BvSZvjullEifdsvHjhLuAn7ROi68zyOQt+DVAzH/uCha4ofifEBeUJ6ILqtE/D9O7dO1W3ePHiRLmmpibV5uKLLy7J9l9//fVEecGC9ARiq1evTtV99dVXJdl+pfAe0AXlCeiC8gR0QVXkMeBVV12Vqps5c2aiPHx4es7M/v37l2T7X375ZaK8cOHCVJvsuZ2bmppKsu3OxntAF5QnoAvKE9AF5QnogqrIk5DslSibq2tNriktVq1alSjnmnsv+6JyrtXQXX68B3RBeQK6oIoZlukrZrqi5bNOSD+gn5ltktQDeAuYRPQA6mEzm5/3xnyO6GpW8DohzQ3LdK5oxQzLhDxWzPTFCl2LzCyvF3AG0e53clzuC3QhSuLZwGN5fIf5q2pf9blyouBhmW1ZMdO55uRzFpxzWKavmOlKoZhhmVMlJVbMbJcIXafmS3W5juJLdbny4wnogvIEdEF5ArqgPAFdUJ6ALqiOfiL6C6Kp3PrEP1eaSoy7XGIekKuyQ68DfrNRqb4SZ0ytxLjLPWbfBbugPAFdUKEScEmg7RarEuMu65iDHAM6d4rvgl1QHZ6AksZK2ippu6SyXF8uHmLwuaT3MurKenHGSl1UskMTUFIXYBEwDhhK9Ezh0JZ/K4gngLFZdeW+OGNFLirZ0T3gcGC7me0ws6PAM0SLHpYVM6sD9mVVTyRalJH4fVKHBtUKM9tjZpvinxuBzEUlyzbujk7A/sAnGeXdVM4Qz4pZnLGSFpX0k5ACWHTpoCwvH2QvKpn5WTnG3dEJ2ABckFE+P66rBHktzhhSMYtKhtLRCfgmMEjSRZK6ATcTLXpYCU4tzghluDhjHotKQhnGnffA9FK9gPHAh8BHwC87evt5xriMaDqSY0THqdOAs4nOIrcBLwO9Q8eZFfMIot3ru8Dm+DW+3OP2OyEuKD8JcUF5ArqgPAFdUJ6ALihPQBeUJ6ALyhPQBeUJ6IL6P4rFfo93cI52AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IJK1BQBVBa8E","executionInfo":{"status":"ok","timestamp":1611287818361,"user_tz":-600,"elapsed":812,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"e3afc0a6-91a5-44fc-8681-cfdae74fb5d0"},"source":["train_X[0]"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,  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160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","         81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,  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0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0]], dtype=uint8)"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"wVoidNVhY8AG"},"source":["**Reminder:** Keep in mind that all features need to be numeric for running a feedforward DNN. When you have some categorical features you have to transform into numerical values such as one-hot encoded."]},{"cell_type":"markdown","metadata":{"id":"N18z9dNhZgrg"},"source":["## Scale the data set\n","\n","The data is in gray scale with each image having a value between 0 and 255. We therefore need to normalize the data by 255."]},{"cell_type":"markdown","metadata":{"id":"7_7haphowdi2"},"source":["input for reshape: 60000, 28, 28\r\n","output of reshape: 60000, 784"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9HK95B47w4bk","executionInfo":{"status":"ok","timestamp":1611287823186,"user_tz":-600,"elapsed":788,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"f9bbc7f4-73e1-4017-f751-47fd7838dc34"},"source":["a = np.array([[[1,2,3],[4,5,6], [5,6,7]], [[1.1,2,3.3],[4,5,6], [5,6,7]]])\r\n","a.shape\r\n","b = a.reshape(2, 9)\r\n","b.shape\r\n","print(a)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["[[[1.  2.  3. ]\n","  [4.  5.  6. ]\n","  [5.  6.  7. ]]\n","\n"," [[1.1 2.  3.3]\n","  [4.  5.  6. ]\n","  [5.  6.  7. ]]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sWGm_2nBxvDx","executionInfo":{"status":"ok","timestamp":1611287824579,"user_tz":-600,"elapsed":728,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"1d025e67-24eb-465b-b346-82f52592afb7"},"source":["print(b)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["[[1.  2.  3.  4.  5.  6.  5.  6.  7. ]\n"," [1.1 2.  3.3 4.  5.  6.  5.  6.  7. ]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YuqRarnsZxuV","executionInfo":{"status":"ok","timestamp":1611287826703,"user_tz":-600,"elapsed":844,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}}},"source":["##### Changing the type\n","train_X = train_X.astype('float32')\n","test_X = test_X.astype('float32')\n","\n","##### Data processing\n","train_X = train_X.reshape(train_X.shape[0], -1)\n","test_X = test_X.reshape(test_X.shape[0], -1)\n","\n","#### Normalizing the data\n","train_X = train_X / 255\n","test_X = test_X / 255"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"snUbpiigYFOH","executionInfo":{"status":"ok","timestamp":1611287831280,"user_tz":-600,"elapsed":773,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"b029f061-531d-4936-a12f-edd30e8f70ae"},"source":["print(train_X.shape, test_X.shape)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["(60000, 784) (10000, 784)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OJSarJhRBrEN","executionInfo":{"status":"ok","timestamp":1611287833535,"user_tz":-600,"elapsed":864,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"ed56cc96-9f4d-4577-c914-28b76f627bdd"},"source":["train_X[0]\n"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.   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      0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        ], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"FPnj_O1-as4i"},"source":["##  Transform the label data\n","\n","For multi-classification model (multinomial response 0 to 9), Keras uses one-hot encoded for the outcome. For example, the digit 5 image that we have plotted above has a label of 5, so for all the digit 5 images, the one hot encoding vector would be $[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]$. "]},{"cell_type":"code","metadata":{"id":"Gi1DFX8daGiF","executionInfo":{"status":"ok","timestamp":1611287836416,"user_tz":-600,"elapsed":798,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}}},"source":["# Change the labels from categorical to one-hot encoding\n","from keras.utils import to_categorical\n","\n","train_Y_one_hot = to_categorical(train_Y)\n","test_Y_one_hot = to_categorical(test_Y)\n"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-yuF-TBraJXb","executionInfo":{"status":"ok","timestamp":1611287836876,"user_tz":-600,"elapsed":600,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"0012dc57-df98-4107-adcf-5521a98dcea1"},"source":["# Cross check\n","print('First image\\'s class:', train_Y[0])\n","print('First image\\'s one hot encoding:', train_Y_one_hot[0])\n"],"execution_count":11,"outputs":[{"output_type":"stream","text":["First image's class: 5\n","First image's one hot encoding: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wy9lF2lza7uC"},"source":["#  Implementation of a DNN using Keras\n","----"]},{"cell_type":"markdown","metadata":{"id":"DZsNkQzVcy9p"},"source":["## Procedure\n","  * Initiate a sequential feed-forward DNN using keras.model.sequential()\n","  * Add some dense layers.\n"]},{"cell_type":"code","metadata":{"id":"Aez-zSqCc8rH","executionInfo":{"status":"ok","timestamp":1611287846064,"user_tz":-600,"elapsed":6149,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}}},"source":["from keras.models import Sequential, Input\n","from keras.layers import Dense\n","\n","model = Sequential()\n","model.add(Input(shape=(train_X[0].size,)))\n","model.add(Dense(128, activation=\"relu\"))\n","model.add(Dense(64, activation=\"relu\"))\n","model.add(Dense(10, activation=\"softmax\"))"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"msgfsexsHuSL"},"source":["Here, we have two hidden layers:\n","\n","  * 128 neurons for the first layer\n","  * 64 for the second\n","  * 10 neurons for the output layer\n","\n","Note that the **shape** in **Input** argument represents the number of features in the data (here 784). It is natural to choose the **softmax** function for the output layer. It is the most common to use **ReLU** activation for hidden layer."]},{"cell_type":"markdown","metadata":{"id":"lgEslnFzFFgQ"},"source":["## Backpropagation and Optimizer\n","\n","We have to define our objective function to optimize and the optimizer to get a solution. The natural choise here is the cross entropy for categorical outcome. In lecture 3 you have learned different variants of the gradient descent. Keras offers several optimizers:\n","\n","  * Stochastic gradient descent (sgd) optimizer\n","  * Adaptive Moment Estimation (adam)\n","  * RMSprop\n","  * Adaptive learning rate (Adadelta)\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SaGcIkXGBgRc","executionInfo":{"status":"ok","timestamp":1611287863529,"user_tz":-600,"elapsed":760,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"4da22431-deee-45ff-890b-741e5db99718"},"source":["# Compile \n","import keras\n","model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.RMSprop(), metrics=['accuracy'])\n","model.summary()"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense (Dense)                (None, 128)               100480    \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 64)                8256      \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 10)                650       \n","=================================================================\n","Total params: 109,386\n","Trainable params: 109,386\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qhySWE6jDOXd"},"source":["## Train our model\n","\n","We will train our model on 25 epochs and a bath size of 128. We also use 20%\n","of our data for the validation step during the training phase, meaning that 60,000×0.2=12,000 of the samples are using for the validation step while 48,000 samples are used for the optimization step.\n","\n","**Reminder:** An epoch describes the number of times the algorithm sees the entire data set. So with a batch size of 128, one epoch is achieved after 48,000/128 = 375 passes."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zMCK604YLDyC","executionInfo":{"status":"ok","timestamp":1611287866928,"user_tz":-600,"elapsed":1440,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"a578fbae-154c-4692-fdfd-60d862b1e8ef"},"source":["# Train-validation Split of the training data\n","from sklearn.model_selection import train_test_split\n","\n","np.random.seed(1)\n","train_train_X, train_valid_X, train_train_label, train_valid_label = train_test_split(train_X, train_Y_one_hot, test_size=0.2, random_state=42, shuffle=True)\n","\n","print(train_train_X.shape, train_valid_X.shape, train_train_label.shape, train_valid_label.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","text":["(48000, 784) (12000, 784) (48000, 10) (12000, 10)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cUX5Bny8MWC_","executionInfo":{"status":"ok","timestamp":1611287899869,"user_tz":-600,"elapsed":32543,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"4e2373ed-fa10-47cd-e86e-e740b1d2f037"},"source":["fit1 = model.fit(train_train_X, train_train_label, batch_size=128, epochs=25, verbose=1, validation_data=(train_valid_X, train_valid_label))"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Epoch 1/25\n","375/375 [==============================] - 4s 4ms/step - loss: 0.5915 - accuracy: 0.8356 - val_loss: 0.1833 - val_accuracy: 0.9457\n","Epoch 2/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1605 - accuracy: 0.9531 - val_loss: 0.1266 - val_accuracy: 0.9618\n","Epoch 3/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1086 - accuracy: 0.9674 - val_loss: 0.1127 - val_accuracy: 0.9673\n","Epoch 4/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0777 - accuracy: 0.9769 - val_loss: 0.0973 - val_accuracy: 0.9722\n","Epoch 5/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0593 - accuracy: 0.9821 - val_loss: 0.0879 - val_accuracy: 0.9747\n","Epoch 6/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9865 - val_loss: 0.0824 - val_accuracy: 0.9759\n","Epoch 7/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0383 - accuracy: 0.9884 - val_loss: 0.0864 - val_accuracy: 0.9761\n","Epoch 8/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0348 - accuracy: 0.9894 - val_loss: 0.0780 - val_accuracy: 0.9792\n","Epoch 9/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0268 - accuracy: 0.9922 - val_loss: 0.0878 - val_accuracy: 0.9755\n","Epoch 10/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0211 - accuracy: 0.9936 - val_loss: 0.0900 - val_accuracy: 0.9763\n","Epoch 11/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0172 - accuracy: 0.9952 - val_loss: 0.0865 - val_accuracy: 0.9782\n","Epoch 12/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0146 - accuracy: 0.9959 - val_loss: 0.0899 - val_accuracy: 0.9798\n","Epoch 13/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0126 - accuracy: 0.9966 - val_loss: 0.1031 - val_accuracy: 0.9773\n","Epoch 14/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0105 - accuracy: 0.9965 - val_loss: 0.1033 - val_accuracy: 0.9773\n","Epoch 15/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0078 - accuracy: 0.9977 - val_loss: 0.0955 - val_accuracy: 0.9797\n","Epoch 16/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.1123 - val_accuracy: 0.9787\n","Epoch 17/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0058 - accuracy: 0.9985 - val_loss: 0.1042 - val_accuracy: 0.9800\n","Epoch 18/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.1051 - val_accuracy: 0.9807\n","Epoch 19/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0047 - accuracy: 0.9988 - val_loss: 0.1290 - val_accuracy: 0.9776\n","Epoch 20/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.1220 - val_accuracy: 0.9797\n","Epoch 21/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1401 - val_accuracy: 0.9744\n","Epoch 22/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.1263 - val_accuracy: 0.9796\n","Epoch 23/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1410 - val_accuracy: 0.9791\n","Epoch 24/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0021 - accuracy: 0.9992 - val_loss: 0.1313 - val_accuracy: 0.9808\n","Epoch 25/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1527 - val_accuracy: 0.9768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"KPTAX9E7Nf3g","executionInfo":{"status":"ok","timestamp":1611287900838,"user_tz":-600,"elapsed":22223,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"80fe399b-e039-420c-806c-225d6ff388cc"},"source":["test_eval = model.evaluate(test_X, test_Y_one_hot, verbose=0)\n","\n","\n","print('Test loss:', test_eval[0])\n","print('Test accuracy:', test_eval[1])\n","\n","accuracy = fit1.history['accuracy']\n","val_accuracy = fit1.history['val_accuracy']\n","loss = fit1.history['loss']\n","val_loss = fit1.history['val_loss']\n","epochs = range(len(accuracy))\n","plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n","plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n","plt.title('Training and validation accuracy')\n","plt.legend()\n","plt.figure()\n","plt.plot(epochs, loss, 'bo', label='Training loss')\n","plt.plot(epochs, val_loss, 'b', label='Validation loss')\n","plt.title('Training and validation loss')\n","plt.legend()\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Test loss: 0.14180326461791992\n","Test accuracy: 0.9781000018119812\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TbiAy9Y4aeoZ"},"source":["We can see that the loss function improves rapidly. However, we can see a potential overfit after 10 epochs. Indeed, the accurary rate of the validation set presents a flat shape after 10 epochs."]},{"cell_type":"markdown","metadata":{"id":"8NmcMvdbcZFd"},"source":["## Prediction\n","\n","We can predict now the class (digits) for a new image."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xImZNnbgbu8S","executionInfo":{"status":"ok","timestamp":1611287908219,"user_tz":-600,"elapsed":963,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"8f58daf3-d9b3-42b7-b105-ffe9fba4d08a"},"source":["predicted_classes = model.predict(test_X[0:20])\n","predicted_classes"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[3.06615895e-16, 4.96388339e-22, 4.67761769e-16, 9.97169795e-12,\n","        5.29131040e-21, 1.07541435e-19, 4.95522227e-28, 1.00000000e+00,\n","        3.62433243e-15, 2.75501044e-09],\n","       [2.15525635e-23, 9.21771408e-14, 1.00000000e+00, 1.07352346e-14,\n","        1.83413916e-37, 5.72200977e-22, 3.68955886e-24, 3.28755818e-27,\n","        6.05428798e-21, 3.98224535e-33],\n","       [2.50287031e-11, 9.99997616e-01, 2.03335571e-08, 5.10373948e-12,\n","        2.40133318e-07, 5.54431168e-10, 7.23478777e-10, 1.82770657e-06,\n","        3.11799937e-07, 5.73493987e-12],\n","       [1.00000000e+00, 1.01424817e-17, 1.87069196e-15, 8.28051551e-18,\n","        7.79857972e-19, 1.01070061e-21, 2.06952119e-16, 1.60588624e-13,\n","        3.36579862e-22, 8.92723666e-13],\n","       [9.75159016e-12, 3.47760591e-20, 5.28977634e-16, 6.79357316e-18,\n","        9.99356687e-01, 6.87825111e-16, 3.49522344e-15, 3.91589561e-09,\n","        2.14425120e-14, 6.43318228e-04],\n","       [5.40442528e-13, 9.99999285e-01, 5.74846106e-13, 2.50817501e-14,\n","        4.25640012e-09, 5.75462583e-13, 6.77711576e-13, 6.59084549e-07,\n","        6.20436413e-09, 2.16510512e-13],\n","       [2.02086903e-18, 6.97436235e-16, 1.53357104e-18, 2.77012489e-18,\n","        9.99999762e-01, 6.98125667e-16, 2.91131265e-21, 5.87919491e-10,\n","        4.22519015e-08, 1.93528308e-07],\n","       [1.90522804e-19, 2.10309895e-15, 6.25076683e-12, 1.00421245e-08,\n","        2.04913899e-13, 2.40345216e-10, 8.04626953e-23, 2.40534034e-11,\n","        2.04166297e-14, 1.00000000e+00],\n","       [6.30355551e-22, 1.71969397e-26, 6.37395343e-19, 4.45170614e-19,\n","        9.74445676e-15, 9.99999523e-01, 4.08167409e-07, 1.32349576e-23,\n","        8.51368753e-08, 8.75764669e-11],\n","       [4.17433904e-28, 7.74733583e-28, 2.90215550e-35, 7.28817044e-21,\n","        6.16862694e-09, 2.41501913e-28, 9.90126532e-32, 4.96932747e-12,\n","        4.00085806e-15, 1.00000000e+00],\n","       [1.00000000e+00, 1.36032959e-24, 2.63099318e-15, 2.82896828e-17,\n","        1.84482049e-28, 4.02528685e-17, 1.12481746e-18, 1.61146643e-18,\n","        1.10744055e-21, 4.53371950e-16],\n","       [7.04033510e-16, 1.76375425e-21, 5.68683207e-19, 1.66608950e-22,\n","        3.40232497e-18, 4.30350498e-13, 1.00000000e+00, 8.04434826e-20,\n","        3.09502505e-13, 3.09665968e-25],\n","       [3.44390797e-22, 2.27852316e-24, 2.36947784e-25, 1.50495773e-13,\n","        9.80394758e-12, 1.35534122e-17, 3.72620495e-25, 2.30411867e-14,\n","        1.31183923e-13, 1.00000000e+00],\n","       [1.00000000e+00, 8.93295386e-22, 6.58523118e-17, 8.55776808e-22,\n","        3.13101645e-22, 5.00754430e-17, 6.84270378e-16, 1.89099951e-12,\n","        1.08482467e-20, 9.79695760e-19],\n","       [3.94671546e-13, 9.99999881e-01, 1.00424139e-11, 1.33326028e-09,\n","        6.11716400e-10, 3.67880119e-12, 6.35318131e-10, 1.66613063e-07,\n","        1.48260169e-08, 4.15549799e-12],\n","       [7.93085086e-27, 4.62508548e-21, 5.94559289e-22, 2.57556865e-10,\n","        2.53658048e-37, 1.00000000e+00, 1.40881218e-22, 1.22667846e-25,\n","        6.13787602e-17, 3.43872758e-19],\n","       [2.66974280e-17, 1.29524884e-27, 7.81406402e-19, 3.04492198e-16,\n","        3.63835481e-13, 1.32369154e-18, 6.79621123e-25, 8.30364477e-13,\n","        1.17923662e-15, 1.00000000e+00],\n","       [1.64016403e-16, 2.18734027e-26, 3.74176043e-17, 1.30691331e-18,\n","        2.96329058e-29, 1.44381612e-24, 2.18065208e-32, 1.00000000e+00,\n","        2.90854475e-24, 1.02613103e-14],\n","       [8.48920491e-18, 5.78441380e-17, 7.13355319e-13, 9.99987602e-01,\n","        2.67190249e-23, 7.13565984e-10, 2.45697380e-19, 2.31270393e-20,\n","        1.23864284e-05, 5.54143620e-09],\n","       [4.63727998e-19, 1.49492116e-14, 7.36805043e-17, 1.52213495e-20,\n","        1.00000000e+00, 1.06171239e-15, 4.08127606e-16, 4.51948123e-09,\n","        1.05200387e-16, 4.19426005e-09]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VjxlDb50PmfN","executionInfo":{"status":"ok","timestamp":1611287950003,"user_tz":-600,"elapsed":928,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"968ca72c-5b1d-4380-f785-4eebb3d186a0"},"source":["sum(predicted_classes[2])"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0000000171013672"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"3DorS-y0ckPK"},"source":["Observe that the predictions are floating point values, it is difficult to compare them with true test labels. So we now round off the output into integers."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Mq98je0eRZz","executionInfo":{"status":"ok","timestamp":1611281709353,"user_tz":-600,"elapsed":756,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"d16d5ad3-a2b0-42ae-efb1-5fee365b05d9"},"source":["predicted_classes = np.argmax(np.round(predicted_classes),axis=1)\n","predicted_classes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 8, 4])"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bb0D5qQnekr8","executionInfo":{"status":"ok","timestamp":1611281737424,"user_tz":-600,"elapsed":801,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"6ca5351a-d9c2-4fa8-c08b-91725d5a23ff"},"source":["# Comparing with true labels\n","test_Y[0:20]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4],\n","      dtype=uint8)"]},"metadata":{"tags":[]},"execution_count":50}]},{"cell_type":"code","metadata":{"id":"rr3xm9Npe-O9"},"source":["# For the entire test data\n","predicted_classes = model.predict(test_X)\n","predicted_classes = np.argmax(np.round(predicted_classes),axis=1)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"86T21wp1gjOo","executionInfo":{"status":"ok","timestamp":1611281840275,"user_tz":-600,"elapsed":810,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"8048cfb6-993c-4de5-e16a-c7bb36b3e7bf"},"source":["np.unique(predicted_classes, return_counts=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n"," array([ 990, 1146, 1057,  986,  988,  892,  950, 1021,  981,  989]))"]},"metadata":{"tags":[]},"execution_count":52}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dpYtBzWvgylC","executionInfo":{"status":"ok","timestamp":1611281868144,"user_tz":-600,"elapsed":816,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"7ae24134-4ffb-4aeb-8ac1-e9780e05dae8"},"source":["np.unique(test_Y, return_counts=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),\n"," array([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009]))"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"id":"IlnxCPsAhVxS"},"source":["import sklearn.metrics\n","cm = sklearn.metrics.confusion_matrix(test_Y, predicted_classes)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7NsUgf9gkXWN","executionInfo":{"status":"ok","timestamp":1611281910261,"user_tz":-600,"elapsed":844,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"fddbb9f3-60f6-4fda-d70b-87543744ad3f"},"source":["print(cm)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 968    2    1    1    1    0    3    1    2    1]\n"," [   0 1125    4    0    0    1    2    1    2    0]\n"," [   4    1 1017    1    2    0    2    3    2    0]\n"," [   2    4   12  968    0    7    0    5    9    3]\n"," [   1    0    0    0  971    0    2    2    1    5]\n"," [   4    0    0    6    1  872    3    1    4    1]\n"," [   6    3    1    1    3    4  934    1    5    0]\n"," [   2    7   10    1    1    0    0  995    6    6]\n"," [   3    2   12    3    1    4    2    3  942    2]\n"," [   0    2    0    5    8    4    2    9    8  971]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rhN3bI7Zk09E","executionInfo":{"status":"ok","timestamp":1611281952150,"user_tz":-600,"elapsed":947,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"5decc250-2d89-4070-f289-8890d30d7bc7"},"source":["print('Accuracy:', sum(np.diag(cm))/test_X.shape[0])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Accuracy: 0.9763\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"V685oNvFk5nS"},"source":["# Improve our model by tuning some parameters\n","-----\n","\n","## Model complexity\n","\n","We will explore different model size by playing with the number of hidden layers from 1 to 3 and different number of neurons. Complex models have higher capacity to learn more features and patterns in the data, however they can overfit the training data. We try to maximize a high validation performance while minimizing the complexity of our model. The folowing table presents the 9 models that we will explore:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"ql3OLiP0laqo","executionInfo":{"status":"ok","timestamp":1611282006440,"user_tz":-600,"elapsed":829,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"7aa42971-4a52-4a2d-d892-c5c2d770e38b"},"source":["from google.colab import data_table\n","import pandas as pd\n","\n","city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n","population = pd.Series([852469, 1015785, 485199])\n","\n","df = pd.DataFrame({'Size':['1 Hidden Layer','2 Hidden Layers','3 Hidden Layers'],\n","                   'Small':[16,(16, 8), (16, 8, 4)],\n","                   'Medium':[64, (64, 32), (64, 32, 16)],\n","                   'Large': [256 , (256, 128), (256, 128, 64)]})\n","\n","data_table.DataTable(df, include_index=False)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.module+javascript":"\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"1 Hidden Layer\",\n16,\n64,\n256],\n [\"2 Hidden Layers\",\n[16, 8],\n[64, 32],\n[256, 128]],\n [\"3 Hidden Layers\",\n[16, 8, 4],\n[64, 32, 16],\n[256, 128, 64]]],\n        columns: [[\"string\", \"Size\"], [\"string\", \"Small\"], [\"string\", \"Medium\"], [\"string\", \"Large\"]],\n        columnOptions: [],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ","text/plain":["<google.colab.data_table.DataTable object>"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Size</th>\n","      <th>Small</th>\n","      <th>Medium</th>\n","      <th>Large</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1 Hidden Layer</td>\n","      <td>16</td>\n","      <td>64</td>\n","      <td>256</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2 Hidden Layers</td>\n","      <td>(16, 8)</td>\n","      <td>(64, 32)</td>\n","      <td>(256, 128)</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3 Hidden Layers</td>\n","      <td>(16, 8, 4)</td>\n","      <td>(64, 32, 16)</td>\n","      <td>(256, 128, 64)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"markdown","metadata":{"id":"LrrDdIJ8MDas"},"source":["We have 9 models to run !!! better to wrap it into a nice function.\n"]},{"cell_type":"code","metadata":{"id":"iW-_Pm_tQXpo"},"source":["def compiler(mdl):\n","  mdl.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.RMSprop(), metrics=['accuracy'])\n","  mdl.summary()\n","  return\n","\n","def trainer(mdl):\n","  ft = mdl.fit(train_train_X, train_train_label, batch_size=128, epochs=25, verbose=1, validation_data=(train_valid_X, train_valid_label))\n","  return ft"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ghihRnukPKJY"},"source":["## One layer model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LveY1jMqPOFW","executionInfo":{"status":"ok","timestamp":1611282235592,"user_tz":-600,"elapsed":83825,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"ba41e075-db9d-425d-a502-89139cd837ea"},"source":["## Small model with 1 layer\n","small_1_layer_model = Sequential()\n","small_1_layer_model.add(Input(shape=(train_X[0].size,)))\n","small_1_layer_model.add(Dense(16, activation=\"relu\"))\n","small_1_layer_model.add(Dense(10, activation=\"softmax\"))\n","compiler(small_1_layer_model)\n","fit_s_1 = trainer(small_1_layer_model)\n","\n","## Medium model with 1 layer\n","medium_1_layer_model = Sequential()\n","medium_1_layer_model.add(Input(shape=(train_X[0].size,)))\n","medium_1_layer_model.add(Dense(64, activation=\"relu\"))\n","medium_1_layer_model.add(Dense(10, activation=\"softmax\"))\n","compiler(medium_1_layer_model)\n","fit_m_1 = trainer(medium_1_layer_model)\n","\n","## Large model with 1 layer\n","large_1_layer_model = Sequential()\n","large_1_layer_model.add(Input(shape=(train_X[0].size,)))\n","large_1_layer_model.add(Dense(256, activation=\"relu\"))\n","large_1_layer_model.add(Dense(10, activation=\"softmax\"))\n","compiler(large_1_layer_model)\n","fit_l_1 = trainer(large_1_layer_model)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense_5 (Dense)              (None, 16)                12560     \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 10)                170       \n","=================================================================\n","Total params: 12,730\n","Trainable params: 12,730\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 3ms/step - loss: 1.0311 - accuracy: 0.7079 - val_loss: 0.3430 - val_accuracy: 0.9057\n","Epoch 2/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3331 - accuracy: 0.9079 - val_loss: 0.2929 - val_accuracy: 0.9178\n","Epoch 3/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2903 - accuracy: 0.9184 - val_loss: 0.2746 - val_accuracy: 0.9243\n","Epoch 4/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.9225 - val_loss: 0.2627 - val_accuracy: 0.9256\n","Epoch 5/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2497 - accuracy: 0.9280 - val_loss: 0.2558 - val_accuracy: 0.9271\n","Epoch 6/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2346 - accuracy: 0.9347 - val_loss: 0.2442 - val_accuracy: 0.9311\n","Epoch 7/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2241 - accuracy: 0.9375 - val_loss: 0.2394 - val_accuracy: 0.9320\n","Epoch 8/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2193 - accuracy: 0.9387 - val_loss: 0.2361 - val_accuracy: 0.9352\n","Epoch 9/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2101 - accuracy: 0.9405 - val_loss: 0.2290 - val_accuracy: 0.9348\n","Epoch 10/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2052 - accuracy: 0.9424 - val_loss: 0.2255 - val_accuracy: 0.9354\n","Epoch 11/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2042 - accuracy: 0.9435 - val_loss: 0.2244 - val_accuracy: 0.9337\n","Epoch 12/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1993 - accuracy: 0.9452 - val_loss: 0.2166 - val_accuracy: 0.9381\n","Epoch 13/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1904 - accuracy: 0.9464 - val_loss: 0.2166 - val_accuracy: 0.9383\n","Epoch 14/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1850 - accuracy: 0.9482 - val_loss: 0.2163 - val_accuracy: 0.9389\n","Epoch 15/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1739 - accuracy: 0.9515 - val_loss: 0.2102 - val_accuracy: 0.9402\n","Epoch 16/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1760 - accuracy: 0.9524 - val_loss: 0.2058 - val_accuracy: 0.9421\n","Epoch 17/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1736 - accuracy: 0.9516 - val_loss: 0.2084 - val_accuracy: 0.9395\n","Epoch 18/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1677 - accuracy: 0.9534 - val_loss: 0.2048 - val_accuracy: 0.9419\n","Epoch 19/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1640 - accuracy: 0.9548 - val_loss: 0.2020 - val_accuracy: 0.9435\n","Epoch 20/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1615 - accuracy: 0.9549 - val_loss: 0.2002 - val_accuracy: 0.9419\n","Epoch 21/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1694 - accuracy: 0.9542 - val_loss: 0.1966 - val_accuracy: 0.9445\n","Epoch 22/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1602 - accuracy: 0.9568 - val_loss: 0.1991 - val_accuracy: 0.9452\n","Epoch 23/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1556 - accuracy: 0.9593 - val_loss: 0.1976 - val_accuracy: 0.9454\n","Epoch 24/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1517 - accuracy: 0.9588 - val_loss: 0.1953 - val_accuracy: 0.9463\n","Epoch 25/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1470 - accuracy: 0.9598 - val_loss: 0.1965 - val_accuracy: 0.9460\n","Model: \"sequential_3\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense_7 (Dense)              (None, 64)                50240     \n","_________________________________________________________________\n","dense_8 (Dense)              (None, 10)                650       \n","=================================================================\n","Total params: 50,890\n","Trainable params: 50,890\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.6947 - accuracy: 0.8190 - val_loss: 0.2751 - val_accuracy: 0.9232\n","Epoch 2/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2396 - accuracy: 0.9311 - val_loss: 0.1996 - val_accuracy: 0.9426\n","Epoch 3/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1832 - accuracy: 0.9477 - val_loss: 0.1681 - val_accuracy: 0.9516\n","Epoch 4/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1519 - accuracy: 0.9573 - val_loss: 0.1499 - val_accuracy: 0.9557\n","Epoch 5/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1292 - accuracy: 0.9635 - val_loss: 0.1307 - val_accuracy: 0.9614\n","Epoch 6/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1135 - accuracy: 0.9665 - val_loss: 0.1297 - val_accuracy: 0.9614\n","Epoch 7/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0920 - accuracy: 0.9737 - val_loss: 0.1199 - val_accuracy: 0.9647\n","Epoch 8/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0891 - accuracy: 0.9745 - val_loss: 0.1116 - val_accuracy: 0.9674\n","Epoch 9/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0736 - accuracy: 0.9790 - val_loss: 0.1118 - val_accuracy: 0.9681\n","Epoch 10/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0673 - accuracy: 0.9810 - val_loss: 0.1061 - val_accuracy: 0.9685\n","Epoch 11/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0605 - accuracy: 0.9826 - val_loss: 0.1036 - val_accuracy: 0.9711\n","Epoch 12/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.1033 - val_accuracy: 0.9700\n","Epoch 13/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0541 - accuracy: 0.9842 - val_loss: 0.0973 - val_accuracy: 0.9722\n","Epoch 14/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9872 - val_loss: 0.0994 - val_accuracy: 0.9713\n","Epoch 15/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0425 - accuracy: 0.9887 - val_loss: 0.0985 - val_accuracy: 0.9715\n","Epoch 16/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9888 - val_loss: 0.1030 - val_accuracy: 0.9713\n","Epoch 17/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0352 - accuracy: 0.9906 - val_loss: 0.0996 - val_accuracy: 0.9713\n","Epoch 18/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0333 - accuracy: 0.9909 - val_loss: 0.1025 - val_accuracy: 0.9712\n","Epoch 19/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9914 - val_loss: 0.1005 - val_accuracy: 0.9730\n","Epoch 20/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9924 - val_loss: 0.1014 - val_accuracy: 0.9724\n","Epoch 21/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0274 - accuracy: 0.9933 - val_loss: 0.1017 - val_accuracy: 0.9718\n","Epoch 22/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9945 - val_loss: 0.1052 - val_accuracy: 0.9719\n","Epoch 23/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0222 - accuracy: 0.9947 - val_loss: 0.1038 - val_accuracy: 0.9724\n","Epoch 24/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0193 - accuracy: 0.9957 - val_loss: 0.1072 - val_accuracy: 0.9717\n","Epoch 25/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0181 - accuracy: 0.9956 - val_loss: 0.1041 - val_accuracy: 0.9726\n","Model: \"sequential_4\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense_9 (Dense)              (None, 256)               200960    \n","_________________________________________________________________\n","dense_10 (Dense)             (None, 10)                2570      \n","=================================================================\n","Total params: 203,530\n","Trainable params: 203,530\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 3ms/step - loss: 0.5196 - accuracy: 0.8558 - val_loss: 0.1838 - val_accuracy: 0.9465\n","Epoch 2/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1501 - accuracy: 0.9573 - val_loss: 0.1198 - val_accuracy: 0.9642\n","Epoch 3/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1019 - accuracy: 0.9705 - val_loss: 0.0991 - val_accuracy: 0.9705\n","Epoch 4/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0723 - accuracy: 0.9790 - val_loss: 0.0866 - val_accuracy: 0.9737\n","Epoch 5/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0555 - accuracy: 0.9837 - val_loss: 0.0812 - val_accuracy: 0.9766\n","Epoch 6/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9866 - val_loss: 0.0792 - val_accuracy: 0.9761\n","Epoch 7/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9905 - val_loss: 0.0826 - val_accuracy: 0.9761\n","Epoch 8/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0286 - accuracy: 0.9921 - val_loss: 0.0778 - val_accuracy: 0.9780\n","Epoch 9/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.9939 - val_loss: 0.0855 - val_accuracy: 0.9757\n","Epoch 10/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0165 - accuracy: 0.9958 - val_loss: 0.0795 - val_accuracy: 0.9779\n","Epoch 11/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0140 - accuracy: 0.9962 - val_loss: 0.1063 - val_accuracy: 0.9722\n","Epoch 12/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0116 - accuracy: 0.9973 - val_loss: 0.0794 - val_accuracy: 0.9797\n","Epoch 13/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0096 - accuracy: 0.9978 - val_loss: 0.0830 - val_accuracy: 0.9793\n","Epoch 14/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0069 - accuracy: 0.9987 - val_loss: 0.0862 - val_accuracy: 0.9781\n","Epoch 15/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0059 - accuracy: 0.9987 - val_loss: 0.0844 - val_accuracy: 0.9816\n","Epoch 16/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.0971 - val_accuracy: 0.9778\n","Epoch 17/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 0.0911 - val_accuracy: 0.9807\n","Epoch 18/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 0.0970 - val_accuracy: 0.9785\n","Epoch 19/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0957 - val_accuracy: 0.9800\n","Epoch 20/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.1010 - val_accuracy: 0.9805\n","Epoch 21/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.1039 - val_accuracy: 0.9807\n","Epoch 22/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1039 - val_accuracy: 0.9812\n","Epoch 23/25\n","375/375 [==============================] - 1s 3ms/step - loss: 8.5745e-04 - accuracy: 0.9999 - val_loss: 0.1030 - val_accuracy: 0.9822\n","Epoch 24/25\n","375/375 [==============================] - 1s 3ms/step - loss: 7.0684e-04 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9798\n","Epoch 25/25\n","375/375 [==============================] - 1s 3ms/step - loss: 6.3807e-04 - accuracy: 0.9998 - val_loss: 0.1216 - val_accuracy: 0.9809\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"HKMC9EI4SgBE"},"source":["## function for plotting the results\n","\n","def plot_results(mdl, ft):\n","  test_eval = mdl.evaluate(test_X, test_Y_one_hot, verbose=0)\n","  print('Test loss:', test_eval[0])\n","  print('Test accuracy:', test_eval[1])\n","\n","  accuracy = ft.history['accuracy']\n","  val_accuracy = ft.history['val_accuracy']\n","  loss = ft.history['loss']\n","  val_loss = ft.history['val_loss']\n","  epochs = range(len(accuracy))\n","  plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n","  plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n","  plt.title('Training and validation accuracy')\n","  plt.legend()\n","  plt.figure()\n","  plt.plot(epochs, loss, 'bo', label='Training loss')\n","  plt.plot(epochs, val_loss, 'b', label='Validation loss')\n","  plt.title('Training and validation loss')\n","  plt.legend()\n","  plt.show()\n","  return\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"Hl5xReomTvA0","executionInfo":{"status":"ok","timestamp":1611282261394,"user_tz":-600,"elapsed":1352,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"5552112a-a69d-4b50-acd6-832ca368bac9"},"source":["## Results for small model with 1 layer\n","plot_results(small_1_layer_model, fit_s_1)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.18719898164272308\n","Test accuracy: 0.9448000192642212\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"y20D2o9fT2vO","executionInfo":{"status":"ok","timestamp":1611282269831,"user_tz":-600,"elapsed":1296,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"84f897d1-6b76-4531-a995-a4f2ec0cece5"},"source":["## Results for medium model with 1 layer\n","plot_results(medium_1_layer_model, fit_m_1)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.10856138914823532\n","Test accuracy: 0.9710999727249146\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"3OBpZca9ULe_","executionInfo":{"status":"ok","timestamp":1611282307710,"user_tz":-600,"elapsed":1568,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"67136e03-6060-4756-a4a3-c81ce59cc5ff"},"source":["## Results for large model with 1 layer\n","plot_results(large_1_layer_model, fit_l_1)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.12158320099115372\n","Test accuracy: 0.9771999716758728\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"UByAUDgjUR_3"},"source":["<font color='red'>**Task 1**: Do the same for models with 2 and 3 hidden layers </font>"]},{"cell_type":"markdown","metadata":{"id":"19UYGipCUwGi"},"source":["<font color='red'>**Task 2**: \n","What are you conclusions from this experiment? which models present some overfit issue? which models to keep? </font>\n"]},{"cell_type":"markdown","metadata":{"id":"VFStLZV5VMJS"},"source":["# Batch normalization\n","-----\n","\n","Here we will add a normalization batch step after each layer. An example using the following code."]},{"cell_type":"code","metadata":{"id":"ZbjTzwazVlaf"},"source":["from keras.layers import BatchNormalization\n","\n","model_w_norm = Sequential()\n","model_w_norm.add(Input(shape=(train_X[0].size,)))\n","model_w_norm.add(BatchNormalization())\n","model_w_norm.add(Dense(128, activation=\"relu\"))\n","model_w_norm.add(BatchNormalization())\n","model_w_norm.add(Dense(64, activation=\"relu\"))\n","model_w_norm.add(BatchNormalization())\n","model_w_norm.add(Dense(10, activation=\"softmax\"))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YHhzeJ6AXOzm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611282476873,"user_tz":-600,"elapsed":40552,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"dd6bcbdd-07cd-4ad8-fc00-f3d6720151ab"},"source":["compiler(model_w_norm)\n","fit_w_norm = trainer(model_w_norm)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_5\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization (BatchNo (None, 784)               3136      \n","_________________________________________________________________\n","dense_11 (Dense)             (None, 128)               100480    \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 128)               512       \n","_________________________________________________________________\n","dense_12 (Dense)             (None, 64)                8256      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 64)                256       \n","_________________________________________________________________\n","dense_13 (Dense)             (None, 10)                650       \n","=================================================================\n","Total params: 113,290\n","Trainable params: 111,338\n","Non-trainable params: 1,952\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.4567 - accuracy: 0.8656 - val_loss: 0.1340 - val_accuracy: 0.9617\n","Epoch 2/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1130 - accuracy: 0.9664 - val_loss: 0.1097 - val_accuracy: 0.9678\n","Epoch 3/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0697 - accuracy: 0.9788 - val_loss: 0.0998 - val_accuracy: 0.9703\n","Epoch 4/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0511 - accuracy: 0.9846 - val_loss: 0.0892 - val_accuracy: 0.9746\n","Epoch 5/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0368 - accuracy: 0.9887 - val_loss: 0.0970 - val_accuracy: 0.9714\n","Epoch 6/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0278 - accuracy: 0.9917 - val_loss: 0.0994 - val_accuracy: 0.9728\n","Epoch 7/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0226 - accuracy: 0.9932 - val_loss: 0.0973 - val_accuracy: 0.9761\n","Epoch 8/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9952 - val_loss: 0.1033 - val_accuracy: 0.9728\n","Epoch 9/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.1012 - val_accuracy: 0.9741\n","Epoch 10/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9958 - val_loss: 0.1023 - val_accuracy: 0.9752\n","Epoch 11/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0112 - accuracy: 0.9968 - val_loss: 0.1047 - val_accuracy: 0.9744\n","Epoch 12/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9977 - val_loss: 0.1116 - val_accuracy: 0.9743\n","Epoch 13/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0093 - accuracy: 0.9966 - val_loss: 0.1100 - val_accuracy: 0.9740\n","Epoch 14/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0074 - accuracy: 0.9974 - val_loss: 0.1123 - val_accuracy: 0.9753\n","Epoch 15/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.0086 - accuracy: 0.9974 - val_loss: 0.1198 - val_accuracy: 0.9743\n","Epoch 16/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0067 - accuracy: 0.9977 - val_loss: 0.1194 - val_accuracy: 0.9755\n","Epoch 17/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 0.1261 - val_accuracy: 0.9746\n","Epoch 18/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.1244 - val_accuracy: 0.9747\n","Epoch 19/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 0.1226 - val_accuracy: 0.9752\n","Epoch 20/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.1338 - val_accuracy: 0.9740\n","Epoch 21/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9981 - val_loss: 0.1352 - val_accuracy: 0.9735\n","Epoch 22/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0059 - accuracy: 0.9981 - val_loss: 0.1298 - val_accuracy: 0.9754\n","Epoch 23/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.1301 - val_accuracy: 0.9754\n","Epoch 24/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.1312 - val_accuracy: 0.9748\n","Epoch 25/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.1361 - val_accuracy: 0.9751\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"Bq9tRcWDYckd","executionInfo":{"status":"ok","timestamp":1611282586097,"user_tz":-600,"elapsed":1638,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"2a5a51b1-939c-461a-898f-007d67c69618"},"source":["plot_results(model_w_norm, fit_w_norm)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.1333978921175003\n","Test accuracy: 0.9739999771118164\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"T6pV_CquH983"},"source":["Now we can explore the batchnormalization on our 9 models"]},{"cell_type":"code","metadata":{"id":"k54YTMtxZL8J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611282786037,"user_tz":-600,"elapsed":105192,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"7734c9ea-d41a-470c-a65e-3d58d81469e6"},"source":["\r\n","# One layer models -----------------------------------------\r\n","## Small model\r\n","\r\n","model_one_small = Sequential()\r\n","model_one_small.add(Input(shape=(train_X[0].size,)))\r\n","model_one_small.add(BatchNormalization())\r\n","model_one_small.add(Dense(16, activation=\"relu\"))\r\n","model_one_small.add(BatchNormalization())\r\n","model_one_small.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_one_small)\r\n","fit_one_small = trainer(model_one_small)\r\n","\r\n","## Medium model\r\n","model_one_medium = Sequential()\r\n","model_one_medium.add(Input(shape=(train_X[0].size,)))\r\n","model_one_medium.add(BatchNormalization())\r\n","model_one_medium.add(Dense(64, activation=\"relu\"))\r\n","model_one_medium.add(BatchNormalization())\r\n","model_one_medium.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_one_medium)\r\n","fit_one_medium = trainer(model_one_medium)\r\n","\r\n","\r\n","## Large model\r\n","model_one_large = Sequential()\r\n","model_one_large.add(Input(shape=(train_X[0].size,)))\r\n","model_one_large.add(BatchNormalization())\r\n","model_one_large.add(Dense(256, activation=\"relu\"))\r\n","model_one_large.add(BatchNormalization())\r\n","model_one_large.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_one_large)\r\n","fit_one_large = trainer(model_one_large)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_6\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_3 (Batch (None, 784)               3136      \n","_________________________________________________________________\n","dense_14 (Dense)             (None, 16)                12560     \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 16)                64        \n","_________________________________________________________________\n","dense_15 (Dense)             (None, 10)                170       \n","=================================================================\n","Total params: 15,930\n","Trainable params: 14,330\n","Non-trainable params: 1,600\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.9159 - accuracy: 0.7418 - val_loss: 0.3197 - val_accuracy: 0.9153\n","Epoch 2/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.2838 - accuracy: 0.9225 - val_loss: 0.2469 - val_accuracy: 0.9289\n","Epoch 3/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.2312 - accuracy: 0.9346 - val_loss: 0.2278 - val_accuracy: 0.9338\n","Epoch 4/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.2102 - accuracy: 0.9405 - val_loss: 0.2117 - val_accuracy: 0.9388\n","Epoch 5/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1923 - accuracy: 0.9449 - val_loss: 0.2012 - val_accuracy: 0.9414\n","Epoch 6/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1757 - accuracy: 0.9487 - val_loss: 0.1965 - val_accuracy: 0.9454\n","Epoch 7/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1653 - accuracy: 0.9518 - val_loss: 0.1905 - val_accuracy: 0.9451\n","Epoch 8/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1606 - accuracy: 0.9534 - val_loss: 0.1871 - val_accuracy: 0.9457\n","Epoch 9/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1496 - accuracy: 0.9560 - val_loss: 0.1822 - val_accuracy: 0.9473\n","Epoch 10/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1454 - accuracy: 0.9564 - val_loss: 0.1815 - val_accuracy: 0.9482\n","Epoch 11/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1392 - accuracy: 0.9578 - val_loss: 0.1832 - val_accuracy: 0.9474\n","Epoch 12/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1395 - accuracy: 0.9588 - val_loss: 0.1777 - val_accuracy: 0.9495\n","Epoch 13/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1294 - accuracy: 0.9610 - val_loss: 0.1801 - val_accuracy: 0.9472\n","Epoch 14/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1294 - accuracy: 0.9615 - val_loss: 0.1749 - val_accuracy: 0.9489\n","Epoch 15/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1270 - accuracy: 0.9622 - val_loss: 0.1746 - val_accuracy: 0.9494\n","Epoch 16/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1224 - accuracy: 0.9620 - val_loss: 0.1784 - val_accuracy: 0.9492\n","Epoch 17/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1202 - accuracy: 0.9641 - val_loss: 0.1820 - val_accuracy: 0.9488\n","Epoch 18/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1179 - accuracy: 0.9646 - val_loss: 0.1754 - val_accuracy: 0.9507\n","Epoch 19/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.1763 - val_accuracy: 0.9507\n","Epoch 20/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1098 - accuracy: 0.9671 - val_loss: 0.1797 - val_accuracy: 0.9492\n","Epoch 21/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1085 - accuracy: 0.9671 - val_loss: 0.1787 - val_accuracy: 0.9503\n","Epoch 22/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1087 - accuracy: 0.9673 - val_loss: 0.1759 - val_accuracy: 0.9504\n","Epoch 23/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1121 - accuracy: 0.9653 - val_loss: 0.1759 - val_accuracy: 0.9513\n","Epoch 24/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1063 - accuracy: 0.9684 - val_loss: 0.1771 - val_accuracy: 0.9501\n","Epoch 25/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1019 - accuracy: 0.9687 - val_loss: 0.1783 - val_accuracy: 0.9513\n","Model: \"sequential_7\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_5 (Batch (None, 784)               3136      \n","_________________________________________________________________\n","dense_16 (Dense)             (None, 64)                50240     \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 64)                256       \n","_________________________________________________________________\n","dense_17 (Dense)             (None, 10)                650       \n","=================================================================\n","Total params: 54,282\n","Trainable params: 52,586\n","Non-trainable params: 1,696\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.5239 - accuracy: 0.8456 - val_loss: 0.1857 - val_accuracy: 0.9488\n","Epoch 2/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1539 - accuracy: 0.9579 - val_loss: 0.1275 - val_accuracy: 0.9629\n","Epoch 3/25\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1032 - accuracy: 0.9708 - val_loss: 0.1159 - val_accuracy: 0.9658\n","Epoch 4/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0811 - accuracy: 0.9758 - val_loss: 0.1085 - val_accuracy: 0.9657\n","Epoch 5/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0682 - accuracy: 0.9812 - val_loss: 0.1061 - val_accuracy: 0.9680\n","Epoch 6/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0586 - accuracy: 0.9832 - val_loss: 0.1021 - val_accuracy: 0.9690\n","Epoch 7/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0497 - accuracy: 0.9860 - val_loss: 0.1043 - val_accuracy: 0.9688\n","Epoch 8/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0436 - accuracy: 0.9868 - val_loss: 0.1049 - val_accuracy: 0.9694\n","Epoch 9/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0348 - accuracy: 0.9906 - val_loss: 0.1038 - val_accuracy: 0.9698\n","Epoch 10/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0317 - accuracy: 0.9907 - val_loss: 0.1037 - val_accuracy: 0.9705\n","Epoch 11/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0279 - accuracy: 0.9915 - val_loss: 0.1090 - val_accuracy: 0.9688\n","Epoch 12/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0249 - accuracy: 0.9922 - val_loss: 0.1121 - val_accuracy: 0.9712\n","Epoch 13/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0218 - accuracy: 0.9935 - val_loss: 0.1136 - val_accuracy: 0.9703\n","Epoch 14/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0195 - accuracy: 0.9939 - val_loss: 0.1145 - val_accuracy: 0.9716\n","Epoch 15/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0195 - accuracy: 0.9938 - val_loss: 0.1143 - val_accuracy: 0.9699\n","Epoch 16/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0182 - accuracy: 0.9936 - val_loss: 0.1165 - val_accuracy: 0.9716\n","Epoch 17/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0155 - accuracy: 0.9954 - val_loss: 0.1168 - val_accuracy: 0.9711\n","Epoch 18/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0137 - accuracy: 0.9963 - val_loss: 0.1211 - val_accuracy: 0.9693\n","Epoch 19/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0139 - accuracy: 0.9954 - val_loss: 0.1203 - val_accuracy: 0.9712\n","Epoch 20/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0122 - accuracy: 0.9969 - val_loss: 0.1275 - val_accuracy: 0.9703\n","Epoch 21/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0108 - accuracy: 0.9970 - val_loss: 0.1289 - val_accuracy: 0.9706\n","Epoch 22/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1263 - val_accuracy: 0.9701\n","Epoch 23/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0097 - accuracy: 0.9973 - val_loss: 0.1330 - val_accuracy: 0.9707\n","Epoch 24/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0092 - accuracy: 0.9974 - val_loss: 0.1378 - val_accuracy: 0.9685\n","Epoch 25/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0087 - accuracy: 0.9978 - val_loss: 0.1420 - val_accuracy: 0.9686\n","Model: \"sequential_8\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_7 (Batch (None, 784)               3136      \n","_________________________________________________________________\n","dense_18 (Dense)             (None, 256)               200960    \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 256)               1024      \n","_________________________________________________________________\n","dense_19 (Dense)             (None, 10)                2570      \n","=================================================================\n","Total params: 207,690\n","Trainable params: 205,610\n","Non-trainable params: 2,080\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.3974 - accuracy: 0.8826 - val_loss: 0.1289 - val_accuracy: 0.9630\n","Epoch 2/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0975 - accuracy: 0.9711 - val_loss: 0.0978 - val_accuracy: 0.9708\n","Epoch 3/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0592 - accuracy: 0.9825 - val_loss: 0.0918 - val_accuracy: 0.9737\n","Epoch 4/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0386 - accuracy: 0.9889 - val_loss: 0.0864 - val_accuracy: 0.9753\n","Epoch 5/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0262 - accuracy: 0.9921 - val_loss: 0.0890 - val_accuracy: 0.9774\n","Epoch 6/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0199 - accuracy: 0.9943 - val_loss: 0.0890 - val_accuracy: 0.9767\n","Epoch 7/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0143 - accuracy: 0.9962 - val_loss: 0.0900 - val_accuracy: 0.9776\n","Epoch 8/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0950 - val_accuracy: 0.9766\n","Epoch 9/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.1011 - val_accuracy: 0.9764\n","Epoch 10/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0079 - accuracy: 0.9979 - val_loss: 0.0982 - val_accuracy: 0.9772\n","Epoch 11/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0067 - accuracy: 0.9981 - val_loss: 0.1020 - val_accuracy: 0.9773\n","Epoch 12/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.1018 - val_accuracy: 0.9787\n","Epoch 13/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0048 - accuracy: 0.9984 - val_loss: 0.1082 - val_accuracy: 0.9772\n","Epoch 14/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1050 - val_accuracy: 0.9778\n","Epoch 15/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1065 - val_accuracy: 0.9777\n","Epoch 16/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1066 - val_accuracy: 0.9782\n","Epoch 17/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.1072 - val_accuracy: 0.9786\n","Epoch 18/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.1072 - val_accuracy: 0.9789\n","Epoch 19/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1144 - val_accuracy: 0.9778\n","Epoch 20/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1103 - val_accuracy: 0.9792\n","Epoch 21/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 0.1210 - val_accuracy: 0.9786\n","Epoch 22/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1208 - val_accuracy: 0.9784\n","Epoch 23/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1156 - val_accuracy: 0.9785\n","Epoch 24/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1207 - val_accuracy: 0.9800\n","Epoch 25/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.1189 - val_accuracy: 0.9786\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"DWNOLWajTI5F","executionInfo":{"status":"ok","timestamp":1611282849574,"user_tz":-600,"elapsed":1605,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"4b3c9f14-f963-4b96-9c0d-78726b6b5802"},"source":["plot_results(model_one_large, fit_one_large)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.12321969866752625\n","Test accuracy: 0.9782000184059143\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t9a4x9ROFYse","executionInfo":{"status":"ok","timestamp":1611283075464,"user_tz":-600,"elapsed":120954,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"8be2d356-6149-48fe-a143-2718fca45aac"},"source":["# Two layer models -----------------------------------------\r\n","## Small model\r\n","\r\n","model_two_small = Sequential()\r\n","model_two_small.add(Input(shape=(train_X[0].size,)))\r\n","model_two_small.add(BatchNormalization())\r\n","model_two_small.add(Dense(16, activation=\"relu\"))\r\n","model_two_small.add(BatchNormalization())\r\n","model_two_small.add(Dense(8, activation=\"relu\"))\r\n","model_two_small.add(BatchNormalization())\r\n","model_two_small.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_two_small)\r\n","fit_two_small = trainer(model_two_small)\r\n","\r\n","## Medium model\r\n","model_two_medium = Sequential()\r\n","model_two_medium.add(Input(shape=(train_X[0].size,)))\r\n","model_two_medium.add(BatchNormalization())\r\n","model_two_medium.add(Dense(64, activation=\"relu\"))\r\n","model_two_medium.add(BatchNormalization())\r\n","model_two_medium.add(Dense(32, activation=\"relu\"))\r\n","model_two_medium.add(BatchNormalization())\r\n","model_two_medium.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_two_medium)\r\n","fit_two_medium = trainer(model_two_medium)\r\n","\r\n","\r\n","## Large model\r\n","model_two_large = Sequential()\r\n","model_two_large.add(Input(shape=(train_X[0].size,)))\r\n","model_two_large.add(BatchNormalization())\r\n","model_two_large.add(Dense(256, activation=\"relu\"))\r\n","model_two_large.add(BatchNormalization())\r\n","model_two_large.add(Dense(128, activation=\"relu\"))\r\n","model_two_large.add(BatchNormalization())\r\n","model_two_large.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_two_large)\r\n","fit_two_large = trainer(model_two_large)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_12\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_19 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dense_30 (Dense)             (None, 16)                12560     \n","_________________________________________________________________\n","batch_normalization_20 (Batc (None, 16)                64        \n","_________________________________________________________________\n","dense_31 (Dense)             (None, 8)                 136       \n","_________________________________________________________________\n","batch_normalization_21 (Batc (None, 8)                 32        \n","_________________________________________________________________\n","dense_32 (Dense)             (None, 10)                90        \n","=================================================================\n","Total params: 16,018\n","Trainable params: 14,402\n","Non-trainable params: 1,616\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 5ms/step - loss: 1.2287 - accuracy: 0.6637 - val_loss: 0.3940 - val_accuracy: 0.9095\n","Epoch 2/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.3433 - accuracy: 0.9130 - val_loss: 0.2532 - val_accuracy: 0.9284\n","Epoch 3/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.2508 - accuracy: 0.9282 - val_loss: 0.2213 - val_accuracy: 0.9345\n","Epoch 4/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.2143 - accuracy: 0.9362 - val_loss: 0.2092 - val_accuracy: 0.9390\n","Epoch 5/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1958 - accuracy: 0.9418 - val_loss: 0.2008 - val_accuracy: 0.9422\n","Epoch 6/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1822 - accuracy: 0.9444 - val_loss: 0.1944 - val_accuracy: 0.9427\n","Epoch 7/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1711 - accuracy: 0.9488 - val_loss: 0.1838 - val_accuracy: 0.9451\n","Epoch 8/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1618 - accuracy: 0.9504 - val_loss: 0.1832 - val_accuracy: 0.9457\n","Epoch 9/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1576 - accuracy: 0.9517 - val_loss: 0.1794 - val_accuracy: 0.9467\n","Epoch 10/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1492 - accuracy: 0.9549 - val_loss: 0.1752 - val_accuracy: 0.9486\n","Epoch 11/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1486 - accuracy: 0.9539 - val_loss: 0.1753 - val_accuracy: 0.9470\n","Epoch 12/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1401 - accuracy: 0.9574 - val_loss: 0.1741 - val_accuracy: 0.9488\n","Epoch 13/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1354 - accuracy: 0.9585 - val_loss: 0.1737 - val_accuracy: 0.9482\n","Epoch 14/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1334 - accuracy: 0.9589 - val_loss: 0.1730 - val_accuracy: 0.9492\n","Epoch 15/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1310 - accuracy: 0.9584 - val_loss: 0.1675 - val_accuracy: 0.9529\n","Epoch 16/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1279 - accuracy: 0.9607 - val_loss: 0.1747 - val_accuracy: 0.9499\n","Epoch 17/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1252 - accuracy: 0.9606 - val_loss: 0.1723 - val_accuracy: 0.9506\n","Epoch 18/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1210 - accuracy: 0.9620 - val_loss: 0.1673 - val_accuracy: 0.9526\n","Epoch 19/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1232 - accuracy: 0.9621 - val_loss: 0.1761 - val_accuracy: 0.9475\n","Epoch 20/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1232 - accuracy: 0.9624 - val_loss: 0.1720 - val_accuracy: 0.9503\n","Epoch 21/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1209 - accuracy: 0.9641 - val_loss: 0.1734 - val_accuracy: 0.9498\n","Epoch 22/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1172 - accuracy: 0.9647 - val_loss: 0.1702 - val_accuracy: 0.9510\n","Epoch 23/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1119 - accuracy: 0.9654 - val_loss: 0.1683 - val_accuracy: 0.9509\n","Epoch 24/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1145 - accuracy: 0.9641 - val_loss: 0.1666 - val_accuracy: 0.9535\n","Epoch 25/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1180 - accuracy: 0.9634 - val_loss: 0.1751 - val_accuracy: 0.9510\n","Model: \"sequential_13\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_22 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dense_33 (Dense)             (None, 64)                50240     \n","_________________________________________________________________\n","batch_normalization_23 (Batc (None, 64)                256       \n","_________________________________________________________________\n","dense_34 (Dense)             (None, 32)                2080      \n","_________________________________________________________________\n","batch_normalization_24 (Batc (None, 32)                128       \n","_________________________________________________________________\n","dense_35 (Dense)             (None, 10)                330       \n","=================================================================\n","Total params: 56,170\n","Trainable params: 54,410\n","Non-trainable params: 1,760\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.6672 - accuracy: 0.8113 - val_loss: 0.1779 - val_accuracy: 0.9496\n","Epoch 2/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1583 - accuracy: 0.9534 - val_loss: 0.1313 - val_accuracy: 0.9602\n","Epoch 3/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1076 - accuracy: 0.9684 - val_loss: 0.1203 - val_accuracy: 0.9662\n","Epoch 4/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0828 - accuracy: 0.9748 - val_loss: 0.1074 - val_accuracy: 0.9670\n","Epoch 5/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0628 - accuracy: 0.9801 - val_loss: 0.1060 - val_accuracy: 0.9684\n","Epoch 6/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0565 - accuracy: 0.9826 - val_loss: 0.1116 - val_accuracy: 0.9678\n","Epoch 7/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0466 - accuracy: 0.9862 - val_loss: 0.1115 - val_accuracy: 0.9695\n","Epoch 8/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0412 - accuracy: 0.9874 - val_loss: 0.1084 - val_accuracy: 0.9704\n","Epoch 9/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0344 - accuracy: 0.9897 - val_loss: 0.1116 - val_accuracy: 0.9689\n","Epoch 10/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0310 - accuracy: 0.9906 - val_loss: 0.1122 - val_accuracy: 0.9709\n","Epoch 11/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0275 - accuracy: 0.9920 - val_loss: 0.1120 - val_accuracy: 0.9706\n","Epoch 12/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0261 - accuracy: 0.9921 - val_loss: 0.1153 - val_accuracy: 0.9700\n","Epoch 13/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0235 - accuracy: 0.9926 - val_loss: 0.1194 - val_accuracy: 0.9704\n","Epoch 14/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0208 - accuracy: 0.9934 - val_loss: 0.1199 - val_accuracy: 0.9696\n","Epoch 15/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9938 - val_loss: 0.1209 - val_accuracy: 0.9708\n","Epoch 16/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0189 - accuracy: 0.9937 - val_loss: 0.1172 - val_accuracy: 0.9722\n","Epoch 17/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9951 - val_loss: 0.1211 - val_accuracy: 0.9716\n","Epoch 18/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9949 - val_loss: 0.1201 - val_accuracy: 0.9705\n","Epoch 19/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9951 - val_loss: 0.1275 - val_accuracy: 0.9712\n","Epoch 20/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0141 - accuracy: 0.9957 - val_loss: 0.1329 - val_accuracy: 0.9712\n","Epoch 21/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.1313 - val_accuracy: 0.9703\n","Epoch 22/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9963 - val_loss: 0.1283 - val_accuracy: 0.9714\n","Epoch 23/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9963 - val_loss: 0.1321 - val_accuracy: 0.9720\n","Epoch 24/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.1416 - val_accuracy: 0.9712\n","Epoch 25/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9965 - val_loss: 0.1417 - val_accuracy: 0.9723\n","Model: \"sequential_14\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_25 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dense_36 (Dense)             (None, 256)               200960    \n","_________________________________________________________________\n","batch_normalization_26 (Batc (None, 256)               1024      \n","_________________________________________________________________\n","dense_37 (Dense)             (None, 128)               32896     \n","_________________________________________________________________\n","batch_normalization_27 (Batc (None, 128)               512       \n","_________________________________________________________________\n","dense_38 (Dense)             (None, 10)                1290      \n","=================================================================\n","Total params: 239,818\n","Trainable params: 237,482\n","Non-trainable params: 2,336\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3732 - accuracy: 0.8864 - val_loss: 0.1119 - val_accuracy: 0.9661\n","Epoch 2/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0870 - accuracy: 0.9738 - val_loss: 0.0986 - val_accuracy: 0.9710\n","Epoch 3/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9849 - val_loss: 0.0836 - val_accuracy: 0.9755\n","Epoch 4/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0363 - accuracy: 0.9899 - val_loss: 0.0880 - val_accuracy: 0.9757\n","Epoch 5/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0240 - accuracy: 0.9927 - val_loss: 0.0900 - val_accuracy: 0.9763\n","Epoch 6/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0185 - accuracy: 0.9943 - val_loss: 0.0886 - val_accuracy: 0.9780\n","Epoch 7/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9963 - val_loss: 0.0893 - val_accuracy: 0.9767\n","Epoch 8/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9958 - val_loss: 0.1041 - val_accuracy: 0.9760\n","Epoch 9/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0102 - accuracy: 0.9967 - val_loss: 0.0983 - val_accuracy: 0.9765\n","Epoch 10/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0084 - accuracy: 0.9971 - val_loss: 0.0942 - val_accuracy: 0.9785\n","Epoch 11/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0101 - accuracy: 0.9966 - val_loss: 0.0925 - val_accuracy: 0.9795\n","Epoch 12/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0069 - accuracy: 0.9979 - val_loss: 0.0988 - val_accuracy: 0.9775\n","Epoch 13/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.1169 - val_accuracy: 0.9772\n","Epoch 14/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0053 - accuracy: 0.9985 - val_loss: 0.1002 - val_accuracy: 0.9791\n","Epoch 15/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.1000 - val_accuracy: 0.9787\n","Epoch 16/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0046 - accuracy: 0.9984 - val_loss: 0.1005 - val_accuracy: 0.9788\n","Epoch 17/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0041 - accuracy: 0.9984 - val_loss: 0.1092 - val_accuracy: 0.9773\n","Epoch 18/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0046 - accuracy: 0.9984 - val_loss: 0.1087 - val_accuracy: 0.9783\n","Epoch 19/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9989 - val_loss: 0.1067 - val_accuracy: 0.9793\n","Epoch 20/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.1150 - val_accuracy: 0.9794\n","Epoch 21/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.1094 - val_accuracy: 0.9799\n","Epoch 22/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0041 - accuracy: 0.9985 - val_loss: 0.1180 - val_accuracy: 0.9784\n","Epoch 23/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.1101 - val_accuracy: 0.9800\n","Epoch 24/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1162 - val_accuracy: 0.9799\n","Epoch 25/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1190 - val_accuracy: 0.9792\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"SqGI7asjURtt","executionInfo":{"status":"ok","timestamp":1611283112226,"user_tz":-600,"elapsed":1733,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"7a21061d-957d-4ab2-c6a6-2a3f8604bc99"},"source":["plot_results(model_two_small, fit_two_small)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.17381563782691956\n","Test accuracy: 0.9521999955177307\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Y1ZbdxjXR4y0"},"source":["# Three layer models -----------------------------------------\r\n","## Small model\r\n","\r\n","model_three_small = Sequential()\r\n","model_three_small.add(Input(shape=(train_X[0].size,)))\r\n","model_three_small.add(BatchNormalization())\r\n","model_three_small.add(Dense(16, activation=\"relu\"))\r\n","model_three_small.add(BatchNormalization())\r\n","model_three_small.add(Dense(8, activation=\"relu\"))\r\n","model_three_small.add(BatchNormalization())\r\n","model_three_small.add(Dense(4, activation=\"relu\"))\r\n","model_three_small.add(BatchNormalization())\r\n","model_three_small.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_three_small)\r\n","fit_three_small = trainer(model_three_small)\r\n","\r\n","## Medium model\r\n","model_three_medium = Sequential()\r\n","model_three_medium.add(Input(shape=(train_X[0].size,)))\r\n","model_three_medium.add(BatchNormalization())\r\n","model_three_medium.add(Dense(64, activation=\"relu\"))\r\n","model_three_medium.add(BatchNormalization())\r\n","model_three_medium.add(Dense(32, activation=\"relu\"))\r\n","model_three_medium.add(BatchNormalization())\r\n","model_three_medium.add(Dense(16, activation=\"relu\"))\r\n","model_three_medium.add(BatchNormalization())\r\n","model_three_medium.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_three_medium)\r\n","fit_three_medium = trainer(model_three_medium)\r\n","\r\n","\r\n","## Large model\r\n","model_three_large = Sequential()\r\n","model_three_large.add(Input(shape=(train_X[0].size,)))\r\n","model_three_large.add(BatchNormalization())\r\n","model_three_large.add(Dense(256, activation=\"relu\"))\r\n","model_three_large.add(BatchNormalization())\r\n","model_three_large.add(Dense(128, activation=\"relu\"))\r\n","model_three_large.add(BatchNormalization())\r\n","model_three_large.add(Dense(64, activation=\"relu\"))\r\n","model_three_large.add(BatchNormalization())\r\n","model_three_large.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_three_large)\r\n","fit_three_large = trainer(model_three_large)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Sh81CkNFVx49"},"source":["plot_results(model_three_small, fit_three_small)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EH66z-6AUoal"},"source":["# Reguralization\r\n","\r\n","Reguralization is generally a good practice for overfitting issues. Here we explore $L_2$ reguralization."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-RxXMxsNVs8G","executionInfo":{"status":"ok","timestamp":1611283265274,"user_tz":-600,"elapsed":35586,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"9d7e424a-46c5-4945-bd6c-99926be6d8b0"},"source":["from keras.regularizers import l2\r\n","\r\n","model_w_reg = Sequential()\r\n","model_w_reg.add(Input(shape=(train_X[0].size,)))\r\n","model_w_reg.add(BatchNormalization())\r\n","model_w_reg.add(Dense(256, activation=\"relu\", kernel_regularizer=l2(0.001)))\r\n","model_w_reg.add(BatchNormalization())\r\n","model_w_reg.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_w_reg)\r\n","fit_w_reg = trainer(model_w_reg)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_15\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_28 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dense_39 (Dense)             (None, 256)               200960    \n","_________________________________________________________________\n","batch_normalization_29 (Batc (None, 256)               1024      \n","_________________________________________________________________\n","dense_40 (Dense)             (None, 10)                2570      \n","=================================================================\n","Total params: 207,690\n","Trainable params: 205,610\n","Non-trainable params: 2,080\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 2s 4ms/step - loss: 0.6395 - accuracy: 0.8873 - val_loss: 0.2834 - val_accuracy: 0.9598\n","Epoch 2/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.2375 - accuracy: 0.9669 - val_loss: 0.1972 - val_accuracy: 0.9698\n","Epoch 3/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1597 - accuracy: 0.9776 - val_loss: 0.1677 - val_accuracy: 0.9720\n","Epoch 4/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1257 - accuracy: 0.9825 - val_loss: 0.1529 - val_accuracy: 0.9732\n","Epoch 5/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1137 - accuracy: 0.9826 - val_loss: 0.1542 - val_accuracy: 0.9716\n","Epoch 6/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1047 - accuracy: 0.9844 - val_loss: 0.1484 - val_accuracy: 0.9730\n","Epoch 7/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.1005 - accuracy: 0.9857 - val_loss: 0.1499 - val_accuracy: 0.9718\n","Epoch 8/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0953 - accuracy: 0.9856 - val_loss: 0.1408 - val_accuracy: 0.9743\n","Epoch 9/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0915 - accuracy: 0.9868 - val_loss: 0.1465 - val_accuracy: 0.9732\n","Epoch 10/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0903 - accuracy: 0.9868 - val_loss: 0.1381 - val_accuracy: 0.9754\n","Epoch 11/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0906 - accuracy: 0.9871 - val_loss: 0.1437 - val_accuracy: 0.9737\n","Epoch 12/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0879 - accuracy: 0.9868 - val_loss: 0.1376 - val_accuracy: 0.9760\n","Epoch 13/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0857 - accuracy: 0.9878 - val_loss: 0.1348 - val_accuracy: 0.9771\n","Epoch 14/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0856 - accuracy: 0.9879 - val_loss: 0.1360 - val_accuracy: 0.9761\n","Epoch 15/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0862 - accuracy: 0.9877 - val_loss: 0.1377 - val_accuracy: 0.9743\n","Epoch 16/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0811 - accuracy: 0.9895 - val_loss: 0.1418 - val_accuracy: 0.9753\n","Epoch 17/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0844 - accuracy: 0.9876 - val_loss: 0.1339 - val_accuracy: 0.9772\n","Epoch 18/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0843 - accuracy: 0.9884 - val_loss: 0.1411 - val_accuracy: 0.9753\n","Epoch 19/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0813 - accuracy: 0.9884 - val_loss: 0.1314 - val_accuracy: 0.9770\n","Epoch 20/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0819 - accuracy: 0.9888 - val_loss: 0.1362 - val_accuracy: 0.9762\n","Epoch 21/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0792 - accuracy: 0.9895 - val_loss: 0.1347 - val_accuracy: 0.9765\n","Epoch 22/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0796 - accuracy: 0.9889 - val_loss: 0.1316 - val_accuracy: 0.9768\n","Epoch 23/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0773 - accuracy: 0.9893 - val_loss: 0.1366 - val_accuracy: 0.9753\n","Epoch 24/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0771 - accuracy: 0.9894 - val_loss: 0.1373 - val_accuracy: 0.9767\n","Epoch 25/25\n","375/375 [==============================] - 1s 4ms/step - loss: 0.0769 - accuracy: 0.9890 - val_loss: 0.1424 - val_accuracy: 0.9764\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"SOWGdejMXkZX","executionInfo":{"status":"ok","timestamp":1611283270375,"user_tz":-600,"elapsed":1558,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"06ba34ad-789e-4a6a-cf75-f860adf5dbf6"},"source":["plot_results(model_w_reg, fit_w_reg)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.13867229223251343\n","Test accuracy: 0.9749000072479248\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZgU1bnH8e/LLoIoi4qgA+6CyDbiFUQg6hVjlIAGQTQiMbjGaK5rNGpIuMaIiXpjcoPXFTBIoqKJGJVFMWKUkUUBBQEHWQQRZBNBmHnvH6d6phlmmO6ZnumZ7t/nefqZ6qpTVW9197x16lTVKXN3REQke9RJdwAiIlK9lPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0QkyyjxZyEze8XMLkt12XQys3wzO7MKlutmdnQ0/L9m9otEylZgPcPM7LWKximSDNN1/LWDmW2Le9sY2AkURO+vdPcJ1R9VzWFm+cAV7j41xct14Bh3X5qqsmbWDvgUqO/uu1MRp0gy6qU7AEmMuzeJDe8ryZlZPSUTqSn0e6yZ1NRTy5lZXzNbZWa3mtla4AkzO8jM/mFm683sq2i4bdw8b5jZFdHwcDP7l5mNicp+ambnVLBsezObaWZbzWyqmT1iZuPLiDuRGH9lZm9Hy3vNzFrGTb/UzFaY2QYzu2Mfn88pZrbWzOrGjRtoZh9Ewz3M7B0z22Rmn5vZH8ysQRnLetLMfh33/uZonjVmNqJE2XPNbK6ZbTGzlWZ2T9zkmdHfTWa2zcxOjX22cfP3NLPZZrY5+tsz0c8myc+5uZk9EW3DV2Y2OW7aADObF23DMjPrH43fo1nNzO6Jfc9m1i5q8vqRmX0GTI/G/zX6HjZHv5GOcfPvZ2YPRN/n5ug3tp+ZvWxmPymxPR+Y2cDStlUSp8SfGQ4FmgM5wEjC9/pE9P4I4BvgD/uY/xRgMdAS+C3wmJlZBco+A7wHtADuAS7dxzoTifFi4HLgYKABcBOAmXUA/hQt/7BofW0phbu/C3wNfKfEcp+JhguAG6PtORU4A7hmH3ETxdA/iucs4Big5PmFr4EfAgcC5wJXm9n3o2mnR38PdPcm7v5OiWU3B14GHo627XfAy2bWosQ27PXZlKK8z3kcoemwY7Ss30cx9ACeBm6OtuF0IL+sz6MUfYATgLOj968QPqeDgTlAfNPkGKA70JPwO74FKASeAi6JFTKzzkAbwmcjleHuetWyF+Ef8MxouC/wLdBoH+W7AF/FvX+D0FQEMBxYGjetMeDAocmUJSSV3UDjuOnjgfEJblNpMd4Z9/4a4J/R8F3AxLhp+0efwZllLPvXwOPRcFNCUs4po+wNwAtx7x04Ohp+Evh1NPw48Ju4csfGly1luQ8Cv4+G20Vl68VNHw78Kxq+FHivxPzvAMPL+2yS+ZyB1oQEe1Ap5f4ci3dfv7/o/T2x7zlu247cRwwHRmWaEXZM3wCdSynXCPiKcN4Ewg7ij9X9/5aJL9X4M8N6d98Re2Nmjc3sz9Gh8xZC08KB8c0dJayNDbj79miwSZJlDwM2xo0DWFlWwAnGuDZueHtcTIfFL9vdvwY2lLUuQu1+kJk1BAYBc9x9RRTHsVHzx9oojv8m1P7Ls0cMwIoS23eKmc2Imlg2A1cluNzYsleUGLeCUNuNKeuz2UM5n/PhhO/sq1JmPRxYlmC8pSn6bMysrpn9Jmou2kLxkUPL6NWotHVFv+lngUvMrA4wlHCEIpWkxJ8ZSl6a9V/AccAp7n4AxU0LZTXfpMLnQHMzaxw37vB9lK9MjJ/HLztaZ4uyCrv7IkLiPIc9m3kgNBl9TKhVHgD8vCIxEI544j0DvAQc7u7NgP+NW255l9KtITTNxDsCWJ1AXCXt63NeSfjODixlvpXAUWUs82vC0V7MoaWUid/Gi4EBhOawZoSjglgMXwI79rGup4BhhCa47V6iWUwqRok/MzUlHD5vitqL767qFUY16DzgHjNrYGanAudVUYx/A75nZqdFJ2JHUf5v+Rngp4TE99cScWwBtpnZ8cDVCcYwCRhuZh2iHU/J+JsSatM7ovbyi+OmrSc0sRxZxrKnAMea2cVmVs/MLgI6AP9IMLaScZT6Obv754S29z9GJ4Hrm1lsx/AYcLmZnWFmdcysTfT5AMwDhkTlc4ELE4hhJ+GorDHhqCoWQyGh2ex3ZnZYdHRwanR0RpToC4EHUG0/ZZT4M9ODwH6E2tS/gX9W03qHEU6QbiC0qz9L+IcvTYVjdPeFwLWEZP45oR14VTmz/YVwwnG6u38ZN/4mQlLeCjwaxZxIDK9E2zAdWBr9jXcNMMrMthLOSUyKm3c7MBp428LVRP9RYtkbgO8RausbCCc7v1ci7kSV9zlfCuwiHPV8QTjHgbu/Rzh5/HtgM/AmxUchvyDU0L8CfsmeR1CleZpwxLUaWBTFEe8m4ENgNrARuI89c9PTQCfCOSNJAd3AJVXGzJ4FPnb3Kj/ikMxlZj8ERrr7aemOJVOoxi8pY2Ynm9lRUdNAf0K77uTy5hMpS9SMdg0wNt2xZBIlfkmlQwmXGm4jXIN+tbvPTWtEUmuZ2dmE8yHrKL85SZKgph4RkSyTUI3fzPqb2WIzW2pmt5UyPcfMpkW3U79R4pbw+8xsQfS6KJXBi4hI8sqt8Uc3eiwh3Jq+inDmfWh0bXSszF+Bf7j7U2b2HeByd7/UzM4lXCVwDtCQ0AxwhrtvKWt9LVu29Hbt2lVqo0REss3777//pbu3SqRsIr1z9iDcpr8cwMwmEk7aLYor0wH4WTQ8g+ITeh2AmR5659ttoWOs/sRd2lZSu3btyMvLSyR2ERGJmFnJu73LlEhTTxv2vDV9FXveOg4wn3ArPMBAoGnUodR8oH9023hLoB+l3M1pZiPNLM/M8tavX59o7CIiUgGpuqrnJqCPmc0l3CSzGihw99cIdyHOItxA8w7FDw8p4u5j3T3X3XNbtUroSEVERCookcS/mj1r6W0p0WeIu69x90Hu3hW4Ixq3Kfo72t27uPtZhL45lqQkchERqZBEEv9s4BgLD9loAAwhdD5VxMxaRr3nAdxO6Hsj1itfi2j4JOAkQM8VFRFJo3JP7rr7bjO7DngVqEvo13yhmY0C8tz9JUKf8PdaeOboTEI/KgD1gbei53RsAS5xPYZNRCStatwNXLm5ua6rekQkm0yYAHfcAZ99BkccAaNHw7BhyS3DzN5399xEyuph6yIiaTRhAowcCdujRxitWBHeQ/LJP1Hqq0ekFpswAdq1gzp1wt8JE8qbQ5KV7GecbPk77ihO+jHbt4fxVSbdz34s+erevbuLSPnGj3dv3Ngdil+NG4fx6TZ+vHtOjrtZ+JtITMnOU13rSOYzrsh3YrZn+djLrPztiUc455pQnk17oi/5UuIXSUxOTukJIycnvXFVJPlVR4KtyDzJfsYV+U5S9T0q8YukWEVql1UtVTXF8iS77dWR/KorwSb7GVfkO0nVkZsSv0gK1dQmleqo8VdX00V1JNiKzFMdOyT31FQslPil1qqOdt5kVdc/c1W3P1dkPdVVs66pNf7qaIJKFSV+qZVq6j9ZdRy+V0cSr8h6qqvpoqa28cfmq2mVkdIo8UutVF2H1VUdVyZtS3U2XdTEq3pqEyV+qZWqo53XvXqaVKprW5KV7Hpq6vkN2VsyiV83cEmNccQRVTseiu+SXLEipLHYXZL7uslm2DAYOxZycsAs/B07dt93VVbHtlREsuupyLZLLZDoHqK6XqrxZ6/qaOetriaVmnq+QjX4zIWaeqQmqIntvNXVpFKR2Kqr/TmT27mzWTKJX71zSkKS7T2wZMdTAI0bp7+ZoF270LxTUk4O5OdXdzQiqZNM75xq489SyXQkVZF28bR0PJWA0aPDDihe48ZhvEi2UOLPQskm8ook8c8+S258ddHJShE9iCUrJdvcUadO2EGUZAaFhalZh4hUjpp6ZJ+SrY1X5FJDNamI1FxK/Bkg2Qc/JJvIK5LE1aQiUnMp8ddyFTnxmmwir2gSHzYsNOsUFoa/SvoiNYPa+Gu5iralp+LhziJScyTTxq/EX8tV5MSriGQendzNItXVx4uIZA4l/lpOV8+ISLKU+GuYZK/Q0dUzIpKseukOQIqV7N8mdoUO7DuRDxumRC8iiUuoxm9m/c1ssZktNbPbSpmeY2bTzOwDM3vDzNrGTfutmS00s4/M7GEzs1RuQCapqf3biEhmKTfxm1ld4BHgHKADMNTMOpQoNgZ42t1PAkYB90bz9gR6AScBJwInA31SFn0tkEzTTU3t30ZEMksiNf4ewFJ3X+7u3wITgQElynQApkfDM+KmO9AIaAA0BOoD6yobdG2R7M1VukJHRKpDIom/DbAy7v2qaFy8+cCgaHgg0NTMWrj7O4QdwefR61V3/6hyIdceyTbd6AodEakOqbqq5yagj5nNJTTlrAYKzOxo4ASgLWFn8R0z611yZjMbaWZ5Zpa3fv36FIWUfsk23egKHRGpDokk/tXA4XHv20bjirj7Gncf5O5dgTuicZsItf9/u/s2d98GvAKcWnIF7j7W3XPdPbdVq1YV3JSapyJNN+rfRkSqWiKJfzZwjJm1N7MGwBDgpfgCZtbSzGLLuh14PBr+jHAkUM/M6hOOBrKmqUdNNyJSE5Wb+N19N3Ad8CohaU9y94VmNsrMzo+K9QUWm9kS4BAgltr+BiwDPiScB5jv7n9P7SbUXGq6EZGaSJ20iYhkAHXSJiIiZVLiFxHJMkr8IiJZRolfJMu4w8qVelBPNlPiF9mHXbtgxgz42c+gY0fo2RN++Uv497+hoCDd0SXv3XfhjDPCvSRdu8LLL5f+BLfK2rgR7r033KU+ZQp89VXq1yEVp6t6kqDn1GaHr76CV16Bv/8d/vlP2LQJGjSAPn3CcF5eSJYHHQRnnglnnx1ebduWv+x0+eij8Nt94QVo1QquuAImTYJly6B375Cke/Wq/Hq++AJ+/3t45BHYuhXq1i3eQXbsGNbRqxecdhq0bx8uc06VnTvDd7dxY/GrrPc5OfCjH8HJJ6c2Bgi/jblz4V//gqZNoXnz8Ftp3rx4eL/9UrtO0DN3q0TJvvIh3Iyl6/Izw5IlIdH//e/hH7agAA4+GM49F847D846C5o0CWW//BKmToXXXoNXX4U1a8L4Dh2gf/+wE+jdu2r+uZP12Wdwzz3w1FOw//5w881www0hIe3aBY89Fo5g1q4N2zl6NHTqlPx6Vq+GMWPgz3+GHTtg8OCwoznySHjvPXj77fCaNQu2bAnzHHpo8Y6gV69wBFK/fvEy3UPZtWvDa9264uH49198EZJ5yX6x4tWpAwceGBLvgQfCokWhfKdO8OMfh//h5s2T3+54a9aEPPH007Bgwb7LNmq0544gNtyhA9x0U8XWr8RfBdq1C71rlpSTE7pWkNqloCAk+FiyX7IkjO/UKSTA886DHj1CwtgXd1i4MOwAXn0VZs4MNc9GjcIRQv/+8MMfVj6pJOvLL+G//zvUvM3g2mvh9tuhZcu9y379NfzP/8BvfhMS7SWXhJ1B+/blryc/H37727ADKSgI895+Oxx3XOnlCwrC5xXbEbz9dvH/z377QffuYYcUS+47d+69jHr14JBDwuvQQ8MOukWLPRNoyYR6wAF7fpdbtsBf/gKPPgrvvw8NG8KFF4YjoT59Ej8K+OYbmDw57Fhffz2cN/mP/4DLLgu/oV27Sj/6KOtIpEOH8DuqiGQSP+5eo17du3f3msjMPfyb7/kyS3dkkqx33nE/6aTw/TVo4H722e7/8z/u+fmVX/bXX7u/8or7DTe4n3BCWMcBB7jfc4/7pk2VX355tmxx/+Uv3Zs2da9Tx33ECPcVKxKbd8MG91tvdW/UyL1+ffef/MR97drSyy5Z4n755e716oWyV17pvnx5xWJevdp90iT3669379XL/ayz3C+91P3mm90feMB9/Hj3qVPdFyxwX7/evaCgYuspy5w57tdc496sWfi+jjnG/b77yt72wkL3mTPdf/Sj8N2C++GHu99xh/vHH6c2tmQAeZ5gnk17oi/5qqmJPyen9MSfk5PuyCRRX33lfvXVYWfdpo37uHEhUVal+fPdBw4Mv5UDD3T/9a+rZp07drg/9JB7q1ZhXYMGuS9aVLFlrVoVEnnduu777+/+i1+4b94cpi1Y4H7xxWGn0qhRSNYrV6ZuO9Lp66/dn3rK/bTTwmdYr174HKdMcd+9233ZMve773Zv3z5M339/98suc582LfU7o4pQ4q8C48e7N268Z9Jv3DiMl9QoLHR/7DH3Qw5x79zZfcIE9127UrPcZ591P/TQkLB++tOqT/glvf+++3nnhd9Nixbuv/mN+7ZtlV9ufr77I48UV0z69XN/993KL9fdffFi94suKo65f//ihHfLLWXXiDPBRx+5/9d/ubdsWbz9sSP8M84IO4itW9Md5Z6U+KvI+PHhH8ws/FXST51Fi9xPPz38Ik89tbiZJCcn1GQrmiSXL3c/55ywrG7d3PPyUhp20t57rzieVq1CU8bXXyc+/7Zt7i+/HGraxx1XXAnp3t39tdfCTi7V3n8/NIcdfHCo/X/5ZerXUVPt3BmaoS66yH306MSbzdJBiV9qje3b3e+8M7QTH3SQ+//9XzhsLihwf+ml0OYL7s2bu991l/sXXyS23G+/DbXq/fZzb9LE/cEHU3P0kCqzZoW2bAhHIg895P7NN3uXKywMzUW//W2oaTZoEOZp1Cgk49/9zn3hwqpJ+FK7KPFLldi9OySZb79NzfJef9396KPDr/CSS9zXrSu93L/+5T5gQHHCu+aa0N5allmz3E88MZQfOLBmt0HPnOnet2+ItU2b0GyzerX7M8+E9uPWrYtr9R07uv/sZ+6vvhp2mCLxlPgl5VauLE5QBx0UktKLL5ZeSy3PunXuw4aFZR19dNgBJGLRonCVSv36oa3+oov2bLrZuDGclIxdZfHii8nHli7Tpxcf3cReBx3kPnhwOO9Rk3deUjMo8UtKPfdcSEL77x+uSrn00uJL35o0CQl40qTyT3YVFLg/+mhYVv36oemmIjuO1avDycXYpXRnnOE+Zkw4KVynTqgV17QTb4koLAzt9Pfd5/7vf4cjLJFEKfFLSmzb5n7FFeFXkpsbrt2O2bkzXK/+4x8XX0LYqJH797/v/vTT4dLJeAsWFNdoTz+94pcaxtu0KSTJWHNIbm44ESmSjZJJ/Lpztxps3AjNmoV+S2qLOXNg6FD45BO49dZwJ2eDBqWX3b073AX73HPw/PPh1vX69UNnYBdcAJ9+Gu7uPOAAeOCBcFdjqvto+fDDcMt/bfqMRVJJXTbUAEuWhET43HPhlvCjjoLrr4fLLw/9pKTS1q2hw60tW0KiPeKIii+rsBB+9zv4+c/DrfDjxkG/fsnN/957xdv+6adh/GWXwf33hw7CRCT1lPjTwD3UOp9/PiS8WCdNPXqE/lqmTg0dVB1wQOgV8Cc/SawvlH2t77334P/+L/Q58vXXxdN69gy19R/8IPRnkqg1a0KCnjoVBg0KHdC1aFG5GOfPD8NdulR8OSJSPvXVU00KC8MNObfeWnxZollow37oIffPPtuz/Lvvug8dGm4Fr1Mn3A4+c2Zy12Bv2BCW3amTF909PGJE6H9m2bJwk0lsWp064Vrxxx/fu829pMmTw92JjRu7jx2r68JFaht0crfq7N4dkvVPfxouGYz16XHWWe7/+7+J3ca+cqX7bbeFq1tid12OGxdOmJamsNB9xoxwCWTDhsUnMv/85+I+VEpasCB0GnXkkV7UGdmAAe4TJ+55p+jXX7tfdVUo07VruFVdRGofJf4qMn26e4cO4VNr2DD0vfLkk6EWXhFffx12FscfH5bZunW4XHL9+jB97dpw92nsaKJZM/drr3WfOzfxdRQWhiONG290P+wwL+prZejQEHusa4SbbgodfYlI7aTEn2KrVrkPGRI+rfbtU9+rY0FBuDTyP//Tiy6L7NMnHEmAe+/e4RLJyt6tuXt3OHIYOTJ0gRDb2bz2Wiq2QkTSKZnEr5O7+7BrFzz8cHiC0a5dcNtt4dLGqnyy0sKFYZ1vvhke5HDFFWU/1KIydu0KJ4dPOKH6HxIiIqmXzMndelUdTG01YwZcd114RNu558JDD4VLMqtax47h8XVVrX791DxjVURqn3IeLJd91qyBiy+G73wnPJPzpZfgH/+onqQvIlIdEkr8ZtbfzBab2VIzu62U6TlmNs3MPjCzN8ysbTS+n5nNi3vtMLPvp3ojUmHXrnDj0vHHh2vx77or1PbPOy/dkYmIpFa5TT1mVhd4BDgLWAXMNrOX3H1RXLExwNPu/pSZfQe4F7jU3WcAXaLlNAeWAq+leBsq7c03w8OoFy6E7343tLGrhi8imSqRGn8PYKm7L3f3b4GJwIASZToA06PhGaVMB7gQeMXdt1c02FTbsgUuuQT69oVt2+DFF9WsIyKZL5HE3wZYGfd+VTQu3nxgUDQ8EGhqZiVv9h8C/KW0FZjZSDPLM7O89evXJxBSatx/PzzzDPziF6FZ5/zzU9t5mIhITZSqk7s3AX3MbC7QB1gNFMQmmllroBPwamkzu/tYd89199xW1dSLV2EhjB8PZ50Fo0ZB48bVsloRkbRL5HLO1cDhce/bRuOKuPsaohq/mTUBLnD3TXFFBgMvuPuuyoWbOrNmQX5+SPoiItkkkRr/bOAYM2tvZg0ITTYvxRcws5ZmFlvW7cDjJZYxlDKaedLl7rtDs84Pfwjt2sGECemOSESkepSb+N19N3AdoZnmI2CSuy80s1Fmdn5UrC+w2MyWAIcAo2Pzm1k7whHDmymNvBKefBKmTw/dBgOsWAEjRyr5i0h2yMouGw4+GEo7h5yTE5p/RERqm2S6bMjKO3fLunDos8+qNw4RkXTIusT/1VdlT6vMIwtFRGqLrEv8f/1r+Nuo0Z7jGzeG0aP3Li8ikmmyLvGPHx/643n00dCmbxb+jh0Lw4alOzoRkaqXVd0y5+fDW2+Fmv0ll4SXiEi2yaoaf+xyzYsvTm8cIiLplDWJ3z008/TuHW7YEhHJVlmT+OfMgY8/hksvTXckIiLplTWJf9w4aNAALrww3ZGIiKRXViT+3bvhL3+B730PDjoo3dGIiKRXViT+qVPhiy/UzCMiAlmS+MeNCzX9c85JdyQiIumX8Yl/61Z44QUYPBgaNkx3NCIi6ZfxiX/yZPjmG92sJSISk/GJf9y4cN1+r17pjkREpGbI6MS/Zg1MmxZq+3qIuohIkNGJf+LE8FB1NfOIiBTL6MQ/bhycfDIcd1y6IxERqTkyNvEvWADz5qm2LyJSUsYm/gkToG5dGDIk3ZGIiNQsGZn4CwtD4j/77PBgdRERKZaRiX/mTFi5Us08IiKlycjEP348NGkCAwakOxIRkZon4xL/N9+EB6pfcEF4gLqIiOwp4xL/P/4BW7aomUdEpCwZl/jHj4fWraFfv3RHIiJSMyWU+M2sv5ktNrOlZnZbKdNzzGyamX1gZm+YWdu4aUeY2Wtm9pGZLTKzdqkLf09ffglTpoSHqdetW1VrERGp3cpN/GZWF3gEOAfoAAw1sw4lio0Bnnb3k4BRwL1x054G7nf3E4AewBepCLw0kyaFp23pgSsiImVLpMbfA1jq7svd/VtgIlDyepkOwPRoeEZserSDqOfurwO4+zZ3356SyEsxfjyceCKcdFJVrUFEpPZLJPG3AVbGvV8VjYs3HxgUDQ8EmppZC+BYYJOZPW9mc83s/ugIYg9mNtLM8swsb/369clvBbB8ObzzjnriFBEpT6pO7t4E9DGzuUAfYDVQANQDekfTTwaOBIaXnNndx7p7rrvntmrVqkIBtG8P//oXDN9r6SIiEq9eAmVWA4fHvW8bjSvi7muIavxm1gS4wN03mdkqYJ67L4+mTQb+A3gsBbHvwUwPWxERSUQiNf7ZwDFm1t7MGgBDgJfiC5hZSzOLLet24PG4eQ80s1g1/jvAosqHLSIiFVVu4nf33cB1wKvAR8Akd19oZqPM7PyoWF9gsZktAQ4BRkfzFhCaeaaZ2YeAAY+mfCtERCRh5u7pjmEPubm5npeXl+4wRERqFTN7391zEymbcXfuiojIvinxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkmYQSv5n1N7PFZrbUzG4rZXqOmU0zsw/M7A0zaxs3rcDM5kWvl1IZvIiIJK9eeQXMrC7wCHAWsAqYbWYvufuiuGJjgKfd/Skz+w5wL3BpNO0bd++S4rhFRKSCEqnx9wCWuvtyd/8WmAgMKFGmAzA9Gp5RynQREakhEkn8bYCVce9XRePizQcGRcMDgaZm1iJ638jM8szs32b2/dJWYGYjozJ569evTyJ8ERFJVqpO7t4E9DGzuUAfYDVQEE3Lcfdc4GLgQTM7quTM7j7W3XPdPbdVq1YpCklEREpTbhs/IYkfHve+bTSuiLuvIarxm1kT4AJ33xRNWx39XW5mbwBdgWWVjlxERCokkRr/bOAYM2tvZg2AIcAeV+eYWUsziy3rduDxaPxBZtYwVgboBcSfFBYRkWpWbuJ3993AdcCrwEfAJHdfaGajzOz8qFhfYLGZLQEOAUZH408A8sxsPuGk729KXA0kIiLVzNw93THsITc31/Py8tIdhohIrWJm70fnU8ulO3dFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZR4hcRyTJK/CIiWUaJX0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr+ISJZJKPGbWX8zW2xmS83stlKm55jZNDP7wMzeMLO2JaYfYGarzOwPqQpcREQqptzEb2Z1gUeAc4AOwFAz61Ci2BjgaeXU5DMAABAqSURBVHc/CRgF3Fti+q+AmZUPV0REKiuRGn8PYKm7L3f3b4GJwIASZToA06PhGfHTzaw7cAjwWuXDFRGRykok8bcBVsa9XxWNizcfGBQNDwSamlkLM6sDPADctK8VmNlIM8szs7z169cnFrmIiFRIqk7u3gT0MbO5QB9gNVAAXANMcfdV+5rZ3ce6e66757Zq1SpFIYmISGnqJVBmNXB43Pu20bgi7r6GqMZvZk2AC9x9k5mdCvQ2s2uAJkADM9vm7nudIBaRve3atYtVq1axY8eOdIciNUSjRo1o27Yt9evXr/AyEkn8s4FjzKw9IeEPAS6OL2BmLYGN7l4I3A48DuDuw+LKDAdylfRFErdq1SqaNm1Ku3btMLN0hyNp5u5s2LCBVatW0b59+wovp9ymHnffDVwHvAp8BExy94VmNsrMzo+K9QUWm9kSwonc0RWOSESK7NixgxYtWijpCwBmRosWLSp9BJhIjR93nwJMKTHurrjhvwF/K2cZTwJPJh2hSJZT0pd4qfg96M5dEZEso8QvkkEmTIB27aBOnfB3woTKLW/Dhg106dKFLl26cOihh9KmTZui999+++0+583Ly+P6668vdx09e/asXJCStISaekSk5pswAUaOhO3bw/sVK8J7gGHDyp5vX1q0aMG8efMAuOeee2jSpAk33VR8W87u3bupV6/0NJKbm0tubm6565g1a1bFgkujgoIC6tatm+4wKkw1fpEMcccdxUk/Zvv2MD6Vhg8fzlVXXcUpp5zCLbfcwnvvvcepp55K165d6dmzJ4sXLwbgjTfe4Hvf+x4QdhojRoygb9++HHnkkTz88MNFy2vSpElR+b59+3LhhRdy/PHHM2zYMNwdgClTpnD88cfTvXt3rr/++qLlxsvPz6d3795069aNbt267bFDue++++jUqROdO3fmttvChYVLly7lzDPPpHPnznTr1o1ly5btETPAddddx5NPPglAu3btuPXWW+nWrRt//etfefTRRzn55JPp3LkzF1xwAdujD3/dunUMHDiQzp0707lzZ2bNmsVdd93Fgw8+WLTcO+64g4ceeqjS30VFqcYvkiE++yy58ZWxatUqZs2aRd26ddmyZQtvvfUW9erVY+rUqfz85z/nueee22uejz/+mBkzZrB161aOO+44rr766r2uRZ87dy4LFy7ksMMOo1evXrz99tvk5uZy5ZVXMnPmTNq3b8/QoUNLjenggw/m9ddfp1GjRnzyyScMHTqUvLw8XnnlFV588UXeffddGjduzMaNGwEYNmwYt912GwMHDmTHjh0UFhaycuXKUpcd06JFC+bMmQOEZrAf//jHANx555089thj/OQnP+H666+nT58+vPDCCxQUFLBt2zYOO+wwBg0axA033EBhYSETJ07kvffeS/pzTxUlfpEMccQRoXmntPGp9oMf/KCoqWPz5s1cdtllfPLJJ5gZu3btKnWec889l4YNG9KwYUMOPvhg1q1bR9u2e3TkS48ePYrGdenShfz8fJo0acKRRx5ZdN360KFDGTt27F7L37VrF9dddx3z5s2jbt26LFmyBICpU6dy+eWX07hxYwCaN2/O1q1bWb16NQMHDgTCTVGJuOiii4qGFyxYwJ133smmTZvYtm0bZ599NgDTp0/n6aefBqBu3bo0a9aMZs2a0aJFC+bOncu6devo2rUrLVq0SGidVUGJXyRDjB69Zxs/QOPGYXyq7b///kXDv/jFL+jXrx8vvPAC+fn59O3bt9R5GjZsWDRct25ddu/eXaEyZfn973/PIYccwvz58yksLEw4mcerV68ehYWFRe9LXi8fv93Dhw9n8uTJdO7cmSeffJI33nhjn8u+4oorePLJJ1m7di0jRoxIOrZUUhu/SIYYNgzGjoWcHDALf8eOrfiJ3URt3ryZNm1Cv42x9vBUOu6441i+fDn5+fkAPPvss2XG0bp1a+rUqcO4ceMoKCgA4KyzzuKJJ54oaoPfuHEjTZs2pW3btkyePBmAnTt3sn37dnJycli0aBE7d+5k06ZNTJs2rcy4tm7dSuvWrdm1axcT4i6fOuOMM/jTn/4EhJPAmzdvBmDgwIH885//ZPbs2UVHB+mixC+SQYYNg/x8KCwMf6s66QPccsst3H777XTt2jWpGnqi9ttvP/74xz/Sv39/unfvTtOmTWnWrNle5a655hqeeuopOnfuzMcff1xUO+/fvz/nn38+ubm5dOnShTFjxgAwbtw4Hn74YU466SR69uzJ2rVrOfzwwxk8eDAnnngigwcPpmvXrmXG9atf/YpTTjmFXr16cfzxxxeNf+ihh5gxYwadOnWie/fuLFq0CIAGDRrQr18/Bg8enPYrgix21rymyM3N9by8vHSHIVIjfPTRR5xwwgnpDiPttm3bRpMmTXB3rr32Wo455hhuvPHGdIeVlMLCwqIrgo455phKLau034WZve/u5V8/i2r8IlILPProo3Tp0oWOHTuyefNmrrzyynSHlJRFixZx9NFHc8YZZ1Q66aeCTu6KSI1344031roafrwOHTqwfPnydIdRRDV+EZEso8QvIpJllPhFRLKMEr+ISJZR4heRMvXr149XX311j3EPPvggV199dZnz9O3bl9gl2d/97nfZtGnTXmXuueeeouvpyzJ58uSia+AB7rrrLqZOnZpM+FIGJX4RKdPQoUOZOHHiHuMmTpxYZkdpJU2ZMoUDDzywQusumfhHjRrFmWeeWaFlpUvs7uGaRolfpJa44Qbo2ze1rxtu2Pc6L7zwQl5++eWih67k5+ezZs0aevfuzdVXX01ubi4dO3bk7rvvLnX+du3a8eWXXwIwevRojj32WE477bSirpuBUrs3njVrFi+99BI333wzXbp0YdmyZQwfPpy//S084XXatGl07dqVTp06MWLECHbu3Fm0vrvvvptu3brRqVMnPv74471iUvfNSvwisg/NmzenR48evPLKK0Co7Q8ePBgzY/To0eTl5fHBBx/w5ptv8sEHH5S5nPfff5+JEycyb948pkyZwuzZs4umDRo0iNmzZzN//nxOOOEEHnvsMXr27Mn555/P/fffz7x58zjqqKOKyu/YsYPhw4fz7LPP8uGHH7J79+6ivnEAWrZsyZw5c7j66qtLbU6Kdd88Z84cnn322aKnhMV33zx//nxuueUWIHTffO211zJ//nxmzZpF69aty/3cYt03DxkypNTtA4q6b54/fz5z5syhY8eOjBgxoqhnz1j3zZdcckm560uWbuASqSXiKoLVKtbcM2DAACZOnFiUuCZNmsTYsWPZvXs3n3/+OYsWLeKkk04qdRlvvfUWAwcOLOoa+fzzzy+aVlb3xmVZvHgx7du359hjjwXgsssu45FHHuGG6PBl0KBBAHTv3p3nn39+r/nVfXMG1fhT/axREQkGDBjAtGnTmDNnDtu3b6d79+58+umnjBkzhmnTpvHBBx9w7rnn7tWFcaKGDx/OH/7wBz788EPuvvvuCi8nJta1c1ndOsd335yXl1fus4NLk2z3zclsX6z75ieeeKLKum/OiMQfe9boihXgXvysUSV/kcpr0qQJ/fr1Y8SIEUUndbds2cL+++9Ps2bNWLduXVFTUFlOP/10Jk+ezDfffMPWrVv5+9//XjStrO6NmzZtytatW/da1nHHHUd+fj5Lly4FQi+bffr0SXh71H1zhiT+6nrWqEi2Gjp0KPPnzy9K/J07d6Zr164cf/zxXHzxxfTq1Wuf83fr1o2LLrqIzp07c84553DyyScXTSure+MhQ4Zw/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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"70oMm7ypYQ9o","executionInfo":{"status":"ok","timestamp":1611283309423,"user_tz":-600,"elapsed":1550,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"a4bd525e-b2cd-47ea-c049-2f022f3b5283"},"source":["plot_results(model_one_large, fit_one_large)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.12321969866752625\n","Test accuracy: 0.9782000184059143\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"9uVaJ0dZYXfH"},"source":["**Question:** Has $L_2$ reguralization improved the performance?\r\n"]},{"cell_type":"markdown","metadata":{"id":"C0cwIrqkZRqw"},"source":["# Dropout\r\n","\r\n","Another possible option for addressing overfitting is the dropout."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Do6uSIVuZtMQ","executionInfo":{"status":"ok","timestamp":1611283486059,"user_tz":-600,"elapsed":49475,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"5d4efbaf-1396-48f7-fd17-41f54cdb92f6"},"source":["from keras.layers import Dropout\r\n","\r\n","model_w_dropout = Sequential()\r\n","model_w_dropout.add(Input(shape=(train_X[0].size,)))\r\n","model_w_dropout.add(BatchNormalization())\r\n","model_w_dropout.add(Dropout(0.4))\r\n","model_w_dropout.add(Dense(256, activation=\"relu\", kernel_regularizer=l2(0.001)))\r\n","model_w_dropout.add(BatchNormalization())\r\n","model_w_dropout.add(Dropout(0.4))\r\n","model_w_dropout.add(Dense(128, activation=\"relu\", kernel_regularizer=l2(0.001)))\r\n","model_w_dropout.add(BatchNormalization())\r\n","model_w_dropout.add(Dropout(0.4))\r\n","model_w_dropout.add(Dense(64, activation=\"relu\", kernel_regularizer=l2(0.001)))\r\n","model_w_dropout.add(BatchNormalization())\r\n","model_w_dropout.add(Dropout(0.4))\r\n","model_w_dropout.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_w_dropout)\r\n","fit_w_dropout = trainer(model_w_dropout)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_16\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_30 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dropout (Dropout)            (None, 784)               0         \n","_________________________________________________________________\n","dense_41 (Dense)             (None, 256)               200960    \n","_________________________________________________________________\n","batch_normalization_31 (Batc (None, 256)               1024      \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 256)               0         \n","_________________________________________________________________\n","dense_42 (Dense)             (None, 128)               32896     \n","_________________________________________________________________\n","batch_normalization_32 (Batc (None, 128)               512       \n","_________________________________________________________________\n","dropout_2 (Dropout)          (None, 128)               0         \n","_________________________________________________________________\n","dense_43 (Dense)             (None, 64)                8256      \n","_________________________________________________________________\n","batch_normalization_33 (Batc (None, 64)                256       \n","_________________________________________________________________\n","dropout_3 (Dropout)          (None, 64)                0         \n","_________________________________________________________________\n","dense_44 (Dense)             (None, 10)                650       \n","=================================================================\n","Total params: 247,690\n","Trainable params: 245,226\n","Non-trainable params: 2,464\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 3s 6ms/step - loss: 1.6981 - accuracy: 0.6315 - val_loss: 0.6335 - val_accuracy: 0.9296\n","Epoch 2/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.7760 - accuracy: 0.8771 - val_loss: 0.4570 - val_accuracy: 0.9511\n","Epoch 3/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.5849 - accuracy: 0.9118 - val_loss: 0.3690 - val_accuracy: 0.9572\n","Epoch 4/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.4899 - accuracy: 0.9208 - val_loss: 0.3209 - val_accuracy: 0.9625\n","Epoch 5/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.4285 - accuracy: 0.9289 - val_loss: 0.2788 - val_accuracy: 0.9678\n","Epoch 6/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.4042 - accuracy: 0.9299 - val_loss: 0.2704 - val_accuracy: 0.9662\n","Epoch 7/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3760 - accuracy: 0.9352 - val_loss: 0.2491 - val_accuracy: 0.9693\n","Epoch 8/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3603 - accuracy: 0.9359 - val_loss: 0.2515 - val_accuracy: 0.9665\n","Epoch 9/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3553 - accuracy: 0.9376 - val_loss: 0.2319 - val_accuracy: 0.9729\n","Epoch 10/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3412 - accuracy: 0.9394 - val_loss: 0.2346 - val_accuracy: 0.9724\n","Epoch 11/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3450 - accuracy: 0.9371 - val_loss: 0.2325 - val_accuracy: 0.9706\n","Epoch 12/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3340 - accuracy: 0.9409 - val_loss: 0.2270 - val_accuracy: 0.9728\n","Epoch 13/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3218 - accuracy: 0.9426 - val_loss: 0.2273 - val_accuracy: 0.9720\n","Epoch 14/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3258 - accuracy: 0.9417 - val_loss: 0.2222 - val_accuracy: 0.9725\n","Epoch 15/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3267 - accuracy: 0.9417 - val_loss: 0.2159 - val_accuracy: 0.9747\n","Epoch 16/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3247 - accuracy: 0.9423 - val_loss: 0.2205 - val_accuracy: 0.9726\n","Epoch 17/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3252 - accuracy: 0.9414 - val_loss: 0.2177 - val_accuracy: 0.9720\n","Epoch 18/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3192 - accuracy: 0.9437 - val_loss: 0.2190 - val_accuracy: 0.9715\n","Epoch 19/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3217 - accuracy: 0.9439 - val_loss: 0.2142 - val_accuracy: 0.9732\n","Epoch 20/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3147 - accuracy: 0.9432 - val_loss: 0.2159 - val_accuracy: 0.9737\n","Epoch 21/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3213 - accuracy: 0.9414 - val_loss: 0.2150 - val_accuracy: 0.9727\n","Epoch 22/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3166 - accuracy: 0.9429 - val_loss: 0.2171 - val_accuracy: 0.9718\n","Epoch 23/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3168 - accuracy: 0.9416 - val_loss: 0.2095 - val_accuracy: 0.9749\n","Epoch 24/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3175 - accuracy: 0.9435 - val_loss: 0.2179 - val_accuracy: 0.9735\n","Epoch 25/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3147 - accuracy: 0.9431 - val_loss: 0.2068 - val_accuracy: 0.9754\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"Bf2CCqrFbBEx","executionInfo":{"status":"ok","timestamp":1611283502999,"user_tz":-600,"elapsed":1637,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"c6331a0d-b489-4b5a-b59c-cc2642755444"},"source":["plot_results(model_w_dropout, fit_w_dropout)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.21065841615200043\n","Test accuracy: 0.9739999771118164\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"HJI6HbKhbWIC","executionInfo":{"status":"error","timestamp":1611283528642,"user_tz":-600,"elapsed":762,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"d7acb7bd-f631-451a-fb7a-3c9bcee23111"},"source":["plot_results(model_three_large, fit_three_large)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-83-84d688872d0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_three_large\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_three_large\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'model_three_large' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"r1HIXjskcKH1"},"source":["# Early stop\r\n","\r\n","You can also adust the number of “epoch” by adding callback_early_stopping(patience = 5) to stop training if the loss has not improved after 5 epochs."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OE1kZBrzct8w","executionInfo":{"status":"ok","timestamp":1611283722370,"user_tz":-600,"elapsed":31493,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"5b686c1f-72b9-4fb6-9bf8-5575c6048d91"},"source":["from keras.callbacks import EarlyStopping\r\n","from keras import initializers\r\n","from keras.callbacks import EarlyStopping\r\n","\r\n","model_w_callback = Sequential()\r\n","model_w_callback.add(Input(shape=(train_X[0].size,)))\r\n","model_w_callback.add(BatchNormalization())\r\n","model_w_callback.add(Dense(256, activation=\"relu\", kernel_initializer=initializers.random_normal(stddev=0.01), kernel_regularizer=l2(0.001)))\r\n","model_w_callback.add(BatchNormalization())\r\n","#model_w_callback.add(Dropout(0.4))\r\n","model_w_callback.add(Dense(128, activation=\"relu\", kernel_initializer=initializers.random_normal(stddev=0.01),  kernel_regularizer=l2(0.001)))\r\n","model_w_callback.add(BatchNormalization())\r\n","#model_w_callback.add(Dropout(0.4))\r\n","model_w_callback.add(Dense(64, activation=\"relu\",kernel_initializer=initializers.random_normal(stddev=0.01), kernel_regularizer=l2(0.001)))\r\n","model_w_callback.add(BatchNormalization())\r\n","#model_w_callback.add(Dropout(0.4))\r\n","model_w_callback.add(Dense(10, activation=\"softmax\"))\r\n","compiler(model_w_callback)\r\n","fit_w_callback = model_w_callback.fit(train_train_X, train_train_label, batch_size=128, epochs=25, verbose=1, validation_data=(train_valid_X, train_valid_label), callbacks=[EarlyStopping(patience=5)])\r\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential_17\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","batch_normalization_34 (Batc (None, 784)               3136      \n","_________________________________________________________________\n","dense_45 (Dense)             (None, 256)               200960    \n","_________________________________________________________________\n","batch_normalization_35 (Batc (None, 256)               1024      \n","_________________________________________________________________\n","dense_46 (Dense)             (None, 128)               32896     \n","_________________________________________________________________\n","batch_normalization_36 (Batc (None, 128)               512       \n","_________________________________________________________________\n","dense_47 (Dense)             (None, 64)                8256      \n","_________________________________________________________________\n","batch_normalization_37 (Batc (None, 64)                256       \n","_________________________________________________________________\n","dense_48 (Dense)             (None, 10)                650       \n","=================================================================\n","Total params: 247,690\n","Trainable params: 245,226\n","Non-trainable params: 2,464\n","_________________________________________________________________\n","Epoch 1/25\n","375/375 [==============================] - 3s 5ms/step - loss: 0.4124 - accuracy: 0.8814 - val_loss: 0.1917 - val_accuracy: 0.9553\n","Epoch 2/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1717 - accuracy: 0.9640 - val_loss: 0.1675 - val_accuracy: 0.9685\n","Epoch 3/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1487 - accuracy: 0.9741 - val_loss: 0.1580 - val_accuracy: 0.9727\n","Epoch 4/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1411 - accuracy: 0.9775 - val_loss: 0.1617 - val_accuracy: 0.9736\n","Epoch 5/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1379 - accuracy: 0.9783 - val_loss: 0.1658 - val_accuracy: 0.9733\n","Epoch 6/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1346 - accuracy: 0.9810 - val_loss: 0.1560 - val_accuracy: 0.9761\n","Epoch 7/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1281 - accuracy: 0.9826 - val_loss: 0.1616 - val_accuracy: 0.9732\n","Epoch 8/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1296 - accuracy: 0.9821 - val_loss: 0.1617 - val_accuracy: 0.9734\n","Epoch 9/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1267 - accuracy: 0.9818 - val_loss: 0.1592 - val_accuracy: 0.9737\n","Epoch 10/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1239 - accuracy: 0.9833 - val_loss: 0.1553 - val_accuracy: 0.9777\n","Epoch 11/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1216 - accuracy: 0.9829 - val_loss: 0.1517 - val_accuracy: 0.9766\n","Epoch 12/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1220 - accuracy: 0.9831 - val_loss: 0.1528 - val_accuracy: 0.9746\n","Epoch 13/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1216 - accuracy: 0.9825 - val_loss: 0.1542 - val_accuracy: 0.9773\n","Epoch 14/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1171 - accuracy: 0.9846 - val_loss: 0.1544 - val_accuracy: 0.9762\n","Epoch 15/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1170 - accuracy: 0.9848 - val_loss: 0.1559 - val_accuracy: 0.9761\n","Epoch 16/25\n","375/375 [==============================] - 2s 5ms/step - loss: 0.1085 - accuracy: 0.9865 - val_loss: 0.1617 - val_accuracy: 0.9735\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5ei1MXeqocxe","executionInfo":{"status":"ok","timestamp":1611283733708,"user_tz":-600,"elapsed":2144,"user":{"displayName":"Sarat Babu Moka","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0tBYIFrEbMM0A4JpqYnWEot2wiDcOXLwOsniSDw=s64","userId":"05092986875825166749"}},"outputId":"e41f1c32-64a7-4545-f6d8-82322ec3e07d"},"source":["plot_results(model_w_callback, fit_w_callback)\r\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Test loss: 0.16950957477092743\n","Test accuracy: 0.9706000089645386\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"PfveqiZUpbOR"},"source":["keras.backend.clear_session()"],"execution_count":null,"outputs":[]}]}